Read Wednesday&#x27;s Scottish <strong>Gossip</strong> in full here.

'Kenneth Vargas finalising permanent Hearts switch' - gossip - BBC Sport

Broadway’s biggest <strong>gossiper</strong> brings &quot;remote work&quot; to a whole new level.

Broadway Gossip Travels Long Distance

A TikTok user turned her storytime about divorcing a &#x27;pathological liar&#x27; into TikTok’s newest hit reality series.

'Who TF Did I Marry' Is the 50-Part Gossip Session Taking Over TikTok

Gossip

From Vietnamese Upbringing to Marketing to a Global Audience: a Candid Convo with Linh Dao Smooke

ভিয়েতনামী লালন-পালন থেকে বিপণন থেকে একটি বিশ্বব্যাপী দর্শক: লিন দাও স্মুকের সাথে একটি অকপট কনভো

Von der vietnamesischen Erziehung zum Marketing für ein globales Publikum: ein offenes Gespräch mit Linh Dao Smooke

De la educación vietnamita al marketing para una audiencia global: una conversación sincera con Linh Dao Smooke

De l'éducation vietnamienne au marketing auprès d'un public mondial : une conversation franche avec Linh Dao Smooke

वियतनामी परवरिश से लेकर वैश्विक दर्शकों के लिए मार्केटिंग तक: लिन्ह दाओ स्मूके के साथ एक स्पष्ट बातचीत

ベトナム育ちから世界のオーディエンスへのマーケティングまで：リン・ダオ・スムークとの率直な会話

베트남에서 자란 것부터 글로벌 청중을 대상으로 하는 마케팅까지: Linh Dao Smooke와의 솔직한 대화

Da educação vietnamita ao marketing para um público global: uma conversa franca com Linh Dao Smooke

От вьетнамского воспитания до маркетинга и глобальной аудитории: откровенная беседа с Линь Дао Смук

Vietnam'da Yetişme Döneminden Küresel Bir Kitleye Pazarlamaya: Linh Dao Smooke ile Samimi Bir Konuşma

Từ nền giáo dục Việt Nam đến tiếp thị đến đối tượng khán giả toàn cầu: Cuộc trò chuyện thẳng thắn với Linh Dao Smooke

从越南成长经历到面向全球受众的营销：与 Linh Dao Smooke 的坦诚对话

Key Takeaways Solana uses a peer-to-peer gossip protocol based on the Plumtree algorithm, allowing decentralized communication between nodes to propagate state changes efficiently across the network without relying on a central source.
Every 0.1 seconds, Solana nodes exchange "push" and "pull" messages, ensuring continuous and up-to-date information sharing to maintain rapid data dissemination and synchronization across the network.
The gossip protocol consists of four main message types—Push, Pull, Prune, and Ping/Pong—that help distribute data, request missing information, manage node connections, and check node vitality.
Gossip data is stored in a Cluster Replicated Data Store (CrdsTable), which is continuously updated with incremental updates to the network's state, ensuring data availability and synchronization among nodes.
Solana's archive system uses "warehouse nodes" to store transaction history, with recent data (1-2 epochs) retained by validators, while full transaction history is stored in Ledger Archives or Google Bigtable instances for easy access by RPCs and other participants. Solana uses a peer-to-peer gossip protocol based on the Plumtree algorithm, allowing decentralized communication between nodes to propagate state changes efficiently across the network without relying on a central source. Every 0.1 seconds, Solana nodes exchange "push" and "pull" messages, ensuring continuous and up-to-date information sharing to maintain rapid data dissemination and synchronization across the network. The gossip protocol consists of four main message types—Push, Pull, Prune, and Ping/Pong—that help distribute data, request missing information, manage node connections, and check node vitality. Gossip data is stored in a Cluster Replicated Data Store (CrdsTable), which is continuously updated with incremental updates to the network's state, ensuring data availability and synchronization among nodes. Solana's archive system uses "warehouse nodes" to store transaction history, with recent data (1-2 epochs) retained by validators, while full transaction history is stored in Ledger Archives or Google Bigtable instances for easy access by RPCs and other participants. Gossip Gossip protocol is a peer-to-peer communication method in distributed systems, where the nodes in the network exchange state information periodically about themselves and other nodes each node is connected to in a decentralized manner. This is similar to how rumors spread on social media. It implies that any slight state change will eventually be propagated through the network, and all nodes will have full information on that state change. On Solana, its implementation of the gossip protocol uses an informal communication method with a tree broadcast approach heavily modeled against the Plumtree algorithm. This makes the propagation of state changes without relying on a central head source. It acts as a control panel for Solana as it aids validators and RPC nodes to know which addresses and ports are open for communication. Solana Gossip works with other major components such as validators, turbine protocol, archive nodes, proof of history, etc. The Validators and RPCs exchange signed data objects every 0.1 seconds over UDP using gossip, guaranteeing information availability throughout the network. Every gossip message has to be at or below the maximum transmission unit (MTU) of 1280 bytes, known as the "packet struct" in the codebase. The gossip protocol serves as a central mechanism for nodes to efficiently manage cluster communication by continuously sharing signed data objects, such as contact information, ledger height, and votes. Every 0.1 seconds, nodes exchange "push" and "pull" messages—where push messages can be forwarded and both types may trigger responses—facilitating rapid data dissemination across the network. Operating over a well-known UDP/IP port or port range, the gossip protocol enables nodes to advertise their gossip endpoints (socket addresses) to one another after the cluster is bootstrapped. This decentralized, high-frequency communication graph ensures Solana’s nodes remain synchronized and resilient, supporting the blockchain’s scalability and performance. In Solana's gossip protocol, four message types facilitate cluster communication: Push messages, the most frequent, distribute data to a select group of "push peers." Pull messages, along with their Pull Responses, are sent periodically to retrieve any missing information by requesting and delivering unshared data. Prune messages enable nodes to trim down their active connections as needed. Lastly, Ping and Pong messages serve as vitality checks, where a ping prompts a pong reply to confirm a peer node’s operational status. Gossip data is stored in a Cluster Replicated Data Store (CrdsTable). This data structure is continuously updated with gossip protocol messages. These messages contain gossip records, which are essentially incremental updates to the CrdsTable. Archive Solana differentiates itself from other blockchains by not requiring the full transaction history to determine an account's current state. Its account model ensures that the state at any given slot is known, enabling validators to store the current state without needing to process all historical blocks. RPCs and validators typically don't retain the entire ledger history; instead, they store only 1 or 2 epochs (2-4 days) of transaction data, which is enough to validate the latest block. Archive data is managed by "warehouse nodes," run by professional RPC service providers, the Solana Foundation, and other ecosystem participants to ensure transaction history is accessible. These warehouse nodes often maintain either a Ledger Archive, which stores raw ledger and AccountsDB snapshots for replaying from scratch, or a Google Bigtable Instance, which stores block data from the genesis block onward in a format suitable for RPC requests. Conclusion In conclusion, Solana's gossip protocol and archive system enable efficient communication and data synchronization across the network. The gossip protocol ensures rapid state propagation, while the archive system stores transaction history for easy access by validators and RPCs. Together, these features support Solana's scalability, performance, and decentralization.

Solana’s Secret to Staying Fast? A Blockchain Gossip Network

Solanas Geheimnis, schnell zu bleiben? Ein Blockchain-Klatschnetzwerk

¿El secreto de Solana para mantenerse rápido? Una red de chismes sobre blockchain.

Solanina tajna da ostane brz? Blockchain mreža tračeva

Solana titka a gyors tartáshoz? Blockchain pletykahálózat

Solana の高速化の秘訣は？ブロックチェーンゴシップネットワーク

អាថ៌កំបាំង​របស់ Solana ក្នុងការ​រក្សា​ភាព​រហ័សរហួន? បណ្តាញនិយាយដើម Blockchain

Solanas noslēpums, kā palikt ātram? Blockchain tenku tīkls

Ny tsiambaratelon'i Solana amin'ny fijanonana haingana? Tambajotra Gossip Blockchain

Sirta Solana si ay u degdegaan? Shabakada xanta ee Blockchain

வேகமாக இருப்பதற்கு சோலானாவின் ரகசியம்? ஒரு பிளாக்செயின் கிசுகிசு நெட்வொர்க்

Xana Xihundla Xa Solana Xa Ku Tshama Hi Ku Tikhoma? Netiweke ya Vuhlekuri bya Blockchain

Секрет Солани залишатися швидким? Блокчейн-мережа пліток

Glossary INV (Inventory): A message sent to nodes in the network to depict the availability of data each node requires. GETDATA: This is a message sent to a node that queries for the data specified in the inv message. Spanning Tree: A spanning tree of a graph is a subset of the graph that connects all its vertices with the minimum number of edges without forming any cycles. Introduction Blockchain is gaining massive attention as support for cryptocurrencies due to the rapid growth of memecoins and the celebrated launch of cryptocurrencies. However, one key challenge in blockchain networks is the efficient propagation of messages between nodes, which leads to communication resource consumption. In traditional gossip protocol, a key problem plaguing it is the latency issue—a node must wait for the next cycle to transmit a message. The Plumtree algorithm is designed to reduce latency and redundancy in message transmission, particularly in peer-to-peer networks and distributed systems where reliable and fast message dissemination is important. It can achieve this by finding the shortest path to send the message to the next node in the spanning tree. By doing this, each node participates in forwarding messages without relying on a central source. This article delves into the implementation of the Plumtree algorithm in the Solana network and its role in increasing the speed of message propagation in the network. Furthermore, a comparison between Plumtree and GossipSub, a gossip-based epidemic broadcast algorithm used in Ethereum. Solana Broadcast In Solana, a gossip-based block propagation method is as follows: when a node receives a new block, it sends a BROADCAST message to other nodes in the network. This broadcast includes information about the new block, such as the block header, transaction data, state changes, and other metadata like compressed data, etc. Upon receiving the BROADCAST message, the receiving node checks if it already has the block in its ledger history. When the nodes check its history and have no record of that block, it sends a QUERY request back to the broadcasting node, querying for the block data. When the node receives the data of the queried block, it verifies it and adds it to the network. This ensures that the node does not share the block data with every other node in the network repeatedly. Plumtree Algorithm Origin The plum is a fruit of the Prunus species, Prunus domestica, a genus of trees that also includes peach, cherry, nectarine, almond, and apricot trees. Plums have a lot of health benefits. They contain a lot of nutrients, are rich in antioxidants, promote bone health, and help lower blood sugar. China and Romania are the two largest plum producers. The main sources of inspiration for the Plumtree algorithm are as follows: The flowering of the plum trees in the early spring.
The transformation of the pollinated flowers to plums.
The plums drop before maturity by 20–30% due to various reasons or diseases, such as diseases caused by plant viruses.
The plums have a shelf life of around 2–6 weeks, even when they are stored at 0 °C. The Plumtree algorithm implements these sources of inspiration as follows: the positions of the flowers and the positions of the plums are represented as matrices; the dropping of the plums before maturity is represented by a fruitiness threshold (FT), and the shelf life of the plums after they are collected is represented by a ripeness threshold (RT). The two thresholds lead to the development of three types of equations to update the positions of the flowers. How Plumtree Works Plumtree is a broadcast method that combines tree-based and gossip-based approaches to reduce redundancy while maintaining high message reliability. In a tree-based broadcast, each node transmits and improves efficiency by forwarding messages only in a tree-based manner. It also uses gossip-based links between nodes to properly handle inter-node failures. In a tree-based broadcast, a tree consisting of all participant nodes is constructed, and messages are transmitted only along the tree, which reduces the redundancy of messages. However, if the number of nodes increases or decreases or if a network failure occurs, the tree becomes incomplete, and a message may not reach all nodes. For instance, the block is broadcasted, and a spanning tree is constructed using the Plumtree algorithm. In subsequent broadcasts, the constructed tree is used. If the exchange of inv and getdata messages is omitted, the blocks are not sent directly to the child nodes of the tree and hence fail. In Gossip-based broadcast, when a node attempts to broadcast a message, the node randomly selects nodes to send the message. The node that receives the message for the first time repeats this process to send the message, which not only provides high scalability but also makes the system more resistant to network inadequacies and node failures. For instance, when a node broadcasts a message, it randomly chooses a node to send it to. The node that receives the message for the first time repeats this process. This property allows the system to respond flexibly to network failures and increases or decreases in the number of nodes. The following approaches are utilized in the Gossip-based broadcast to disseminate data: Eager push: In this approach, once a message is received, it is sent to the neighboring nodes immediately.
Lazy push: In this approach, when a node receives a message, it sends the message identifier to the neighboring nodes. If the node has not received the message, it makes a pull request to the neighboring nodes.
Pull: Once a node has not received a message, it makes a query to its neighboring nodes—pull on recently received messages to update its state with that particular message. Plumtree uses a combination of an Eager push mechanism for sending a small number of messages and a Lazy push mechanism for assisting transmission. Plumtree in Solana Network Solana utilizes a peer-to-peer communication approach combined with a Plumtree tree-inspired algorithm, allowing for efficient dissemination of data throughout the network of nodes without depending on a central source. This structure enables messages within the network to be disseminated hierarchically, with nodes directly interacting and making each node in the network operate autonomously in relaying messages to the subsequent node in the tree. A spanning tree structure is constructed using the Plumtree algorithm for the nodes in the Solana network, allowing for a more scalable approach to broadcasting information throughout the network. In basic terms, every node shares messages with its direct neighbors, who in turn, propagate the information across the spanning tree. Differences Between Plumtree Algorithm and GossipSub in Ethereum The differences between the Plumtree algorithm and GossipSub used for information broadcasting in Solana and Ethereum, respectively, include: Solana Tree-based PTA uses a tree-based broadcast approach where nodes are connected in a hierarchical manner where the parent node is connected to the child node. The focus is on building an optimal routing path over time through message duplication handling, while GossipSub utilizes topic-based P2P overlays, where nodes subscribe to specific topics related to validator duties and communication within the Ethereum network. These overlays self-optimize to find the shortest path for message dissemination. Solana Tree-based PTA uses epidemic broadcast but focuses on handling message redundancy by downgrading peers with suboptimal message paths, while in GossipSub, messages are disseminated via a random epidemic broadcast. Wrapping Up In this article, we examined the Plumtree algorithm in Solana's Gossip Protocol, highlighting its tree-based and gossip-based approach to efficient message dissemination. We also compared it with Ethereum’s GossipSub, noting its differences. Glossary INV (Inventory): A message sent to nodes in the network to depict the availability of data each node requires. INV (Inventory): GETDATA : This is a message sent to a node that queries for the data specified in the inv message. GETDATA inv inv Spanning Tree : A spanning tree of a graph is a subset of the graph that connects all its vertices with the minimum number of edges without forming any cycles. Spanning Tree spanning tree Introduction Blockchain is gaining massive attention as support for cryptocurrencies due to the rapid growth of memecoins and the celebrated launch of cryptocurrencies. However, one key challenge in blockchain networks is the efficient propagation of messages between nodes, which leads to communication resource consumption. In traditional gossip protocol, a key problem plaguing it is the latency issue—a node must wait for the next cycle to transmit a message. The Plumtree algorithm is designed to reduce latency and redundancy in message transmission, particularly in peer-to-peer networks and distributed systems where reliable and fast message dissemination is important. It can achieve this by finding the shortest path to send the message to the next node in the spanning tree . By doing this, each node participates in forwarding messages without relying on a central source. spanning tree spanning tree This article delves into the implementation of the Plumtree algorithm in the Solana network and its role in increasing the speed of message propagation in the network. Furthermore, a comparison between Plumtree and GossipSub, a gossip-based epidemic broadcast algorithm used in Ethereum. Solana Broadcast In Solana, a gossip-based block propagation method is as follows: when a node receives a new block, it sends a BROADCAST message to other nodes in the network. This broadcast includes information about the new block, such as the block header, transaction data, state changes, and other metadata like compressed data, etc. Upon receiving the BROADCAST message, the receiving node checks if it already has the block in its ledger history. When the nodes check its history and have no record of that block, it sends a QUERY request back to the broadcasting node, querying for the block data. BROADCAST BROADCAST QUERY When the node receives the data of the queried block, it verifies it and adds it to the network. This ensures that the node does not share the block data with every other node in the network repeatedly. Plumtree Algorithm Origin The plum is a fruit of the Prunus species, Prunus domestica , a genus of trees that also includes peach, cherry, nectarine, almond, and apricot trees. Plums have a lot of health benefits. They contain a lot of nutrients, are rich in antioxidants, promote bone health, and help lower blood sugar. China and Romania are the two largest plum producers. Prunus domestica The main sources of inspiration for the Plumtree algorithm are as follows: The flowering of the plum trees in the early spring. The transformation of the pollinated flowers to plums. The plums drop before maturity by 20–30% due to various reasons or diseases, such as diseases caused by plant viruses. The plums have a shelf life of around 2–6 weeks, even when they are stored at 0 °C. The flowering of the plum trees in the early spring. The transformation of the pollinated flowers to plums. The plums drop before maturity by 20–30% due to various reasons or diseases, such as diseases caused by plant viruses. The plums have a shelf life of around 2–6 weeks, even when they are stored at 0 °C. The Plumtree algorithm implements these sources of inspiration as follows: the positions of the flowers and the positions of the plums are represented as matrices; the dropping of the plums before maturity is represented by a fruitiness threshold (FT), and the shelf life of the plums after they are collected is represented by a ripeness threshold (RT). The two thresholds lead to the development of three types of equations to update the positions of the flowers. fruitiness threshold fruitiness threshold ripeness threshold ripeness threshold How Plumtree Works Plumtree is a broadcast method that combines tree-based and gossip-based approaches to reduce redundancy while maintaining high message reliability. In a tree-based broadcast, each node transmits and improves efficiency by forwarding messages only in a tree-based manner. It also uses gossip-based links between nodes to properly handle inter-node failures. In a tree-based broadcast, a tree consisting of all participant nodes is constructed, and messages are transmitted only along the tree, which reduces the redundancy of messages. However, if the number of nodes increases or decreases or if a network failure occurs, the tree becomes incomplete, and a message may not reach all nodes. For instance, the block is broadcasted, and a spanning tree is constructed using the Plumtree algorithm. In subsequent broadcasts, the constructed tree is used. If the exchange of inv and getdata messages is omitted, the blocks are not sent directly to the child nodes of the tree and hence fail. spanning tree spanning tree inv inv getdata getdata In Gossip-based broadcast, when a node attempts to broadcast a message, the node randomly selects nodes to send the message. The node that receives the message for the first time repeats this process to send the message, which not only provides high scalability but also makes the system more resistant to network inadequacies and node failures. For instance, when a node broadcasts a message, it randomly chooses a node to send it to. The node that receives the message for the first time repeats this process. This property allows the system to respond flexibly to network failures and increases or decreases in the number of nodes. The following approaches are utilized in the Gossip-based broadcast to disseminate data: Eager push: In this approach, once a message is received, it is sent to the neighboring nodes immediately. Lazy push: In this approach, when a node receives a message, it sends the message identifier to the neighboring nodes. If the node has not received the message, it makes a pull request to the neighboring nodes. Pull: Once a node has not received a message, it makes a query to its neighboring nodes—pull on recently received messages to update its state with that particular message. Eager push : In this approach, once a message is received, it is sent to the neighboring nodes immediately. Eager push Lazy push : In this approach, when a node receives a message, it sends the message identifier to the neighboring nodes. If the node has not received the message, it makes a pull request to the neighboring nodes. Lazy push Pull : Once a node has not received a message, it makes a query to its neighboring nodes— pull on recently received messages to update its state with that particular message. Pull pull pull Plumtree uses a combination of an Eager push mechanism for sending a small number of messages and a Lazy push mechanism for assisting transmission. Plumtree in Solana Network Solana utilizes a peer-to-peer communication approach combined with a Plumtree tree-inspired algorithm, allowing for efficient dissemination of data throughout the network of nodes without depending on a central source. This structure enables messages within the network to be disseminated hierarchically, with nodes directly interacting and making each node in the network operate autonomously in relaying messages to the subsequent node in the tree. A spanning tree structure is constructed using the Plumtree algorithm for the nodes in the Solana network, allowing for a more scalable approach to broadcasting information throughout the network. In basic terms, every node shares messages with its direct neighbors, who in turn, propagate the information across the spanning tree. Differences Between Plumtree Algorithm and GossipSub in Ethereum The differences between the Plumtree algorithm and GossipSub used for information broadcasting in Solana and Ethereum, respectively, include: Solana Tree-based PTA uses a tree-based broadcast approach where nodes are connected in a hierarchical manner where the parent node is connected to the child node. The focus is on building an optimal routing path over time through message duplication handling, while GossipSub utilizes topic-based P2P overlays, where nodes subscribe to specific topics related to validator duties and communication within the Ethereum network. These overlays self-optimize to find the shortest path for message dissemination. Solana Tree-based PTA uses a tree-based broadcast approach where nodes are connected in a hierarchical manner where the parent node is connected to the child node. The focus is on building an optimal routing path over time through message duplication handling, while GossipSub utilizes topic-based P2P overlays , where nodes subscribe to specific topics related to validator duties and communication within the Ethereum network. These overlays self-optimize to find the shortest path for message dissemination. tree-based broadcast topic-based P2P overlays Solana Tree-based PTA uses epidemic broadcast but focuses on handling message redundancy by downgrading peers with suboptimal message paths, while in GossipSub, messages are disseminated via a random epidemic broadcast. Solana Tree-based PTA uses epidemic broadcast but focuses on handling message redundancy by downgrading peers with suboptimal message paths, while in GossipSub, messages are disseminated via a random epidemic broadcast . epidemic broadcast random epidemic broadcast Wrapping Up In this article, we examined the Plumtree algorithm in Solana's Gossip Protocol, highlighting its tree-based and gossip-based approach to efficient message dissemination. We also compared it with Ethereum’s GossipSub, noting its differences.

The PlumTree Algorithm (PTA) in the Solana Gossip Protocol: Some Thoughts

Authors:
(1) Esteban Villa-Turek, Corresponding author;
(2) Rod Abhari, Collaborator;
(3) Erik C. Nisbet, Collaborator;
(4) Yu Xu, Collaborator;
(5) Ayse Deniz Lokmanoglu, Collaborator. Table of Links Abstract Transnational Network Dynamics of Problematic Information Diffusion Theoretical Framework Methodology Statistical Analysis and Results Discussion and Limitations Conclusions and Policy Implications, and References Statistical Analysis and Results After analyzing the main brokers in the network qualitatively, the study employed Exponential Random Graph Models (ERGMs) to statistically determine which nodal attributes are significant in explaining the emergence of network ties. ERGMs are particularly well suited for this task, as they allow researchers to statistically assess and predict the structural characteristics of ties in an observed network. This operates by simulating randomly-generated networks that are specified to structurally equate the observed network, allowing to conduct hypothesis tests on the observed network’s attributes (Robins & Lusher, 2013). In other words, ERGMs are useful to compare in this study the relative influence of transnational factors on tie formation in the networks to other non-transnational actor-level factors to contrast their predictive power around the emergence dynamics of the networks (Robins et al., 2007). In fact, ERGMs have been emphasized in the literature as an ideal method for social network analysis in communication studies, mainly because they allow researchers to employ structure, external relationships or attributes as predictors, all while accounting for the interdependent nature of network data (Shumate & Palazzolo, 2010). Therefore, the study estimates two ERGMs on the two networks using the ergm package version 3.9.4, as implemented for the R programing language (Handcock et al., 2018). To estimate ERGMs for both networks, the study makes use of endogenous attributes as mechanisms through which the network is structured, as well as of exogenous attributes of the nodes and of exogenous relations in the network (Contractor et al., 2006). In general, endogenous attributes correspond to those self-organizing factors in a network that are merely structural and do not depend on attributes of nodes or edges, like the number of edges or triangles. Conversely, exogenous attributes are the ones that help explain the formation of ties in a network based on nonstructural attributes, like attributes of nodes and edges, or other relationships between the nodes that exist in other networks, and that could help explain the emergence of ties in the observed network. We hypothesize, as mentioned above, that linguistic similarity, thematic similarity, and cultural and geographic proximity increase the likelihood of tie formation in both networks. If the hypotheses hold, it would be expected to see a higher likelihood in co-sharing activity in the network between groups that share the same language, the same themes, and that are culturally or geographically close to one another, as captured by the SCI (Bailey et al., 2018). Exogenous attributes are employed to estimate the influence of non-structural characteristics of the network on its emergence, like node characteristics or external network relationships. The exogenous nodal attributes to test for linguistic and thematic similarity are given by the hand-coded language and group category labels introduced above. The exogenous relations in the network to account for cultural similarity are modeled using the SCI, which is calculated by taking the number of Facebook connections between two geographic regions and dividing them by the product of Facebook users in both regions, which is then scaled to have a maximum value of 1,000,000,000 (maximum connectedness) and 1 (minimum connectedness), accounting for every possible country-country pair and without having any missing values (Facebook Data For Good Social Connectedness Index Methodology, 2021). This study employs the SCI to account for cultural similarity relations in social networks, especially those that emerge across national borders. As mentioned above, the SCI empirically shows that people across the world tend to form ties with those who are most similar to them, based for instance on sociodemographic characteristics like race, age, religion, education, but also on other “intangible aspects such as attitudes and beliefs (…)”, which can be understood, among others, as cultural in nature (Bailey et al., 2018). To reiterate, the SCI has proven useful to correlate transnational connectedness with historic intranational and international migratory patterns (Bailey et al., 2018), a relationship that might help account for the emergence of ties between seemingly distant (or close) countries in both networks under study. Finally, the nodal attribute to model geographic co-location is given by the hand-coded domain label discussed above. MPV ERGM Table 1 presents the estimated ERGM coefficients. Table 1 ERGM coefficients for the MPV network The interpretation of ERGM coefficients is similar to that of a logistic regression. In that sense, they allow us to assess whether each term increases or decreases the likelihood of tie formation in the networks. In terms of geographical co-location, the results do not indicate a statistically significant effect on the likelihood of tie formation. However, the same cannot be said of cultural similarity, which shows a positive and significant effect, indicating that cultural similarity between groups’ nationalities increases the likelihood of tie formation in the network, holding everything else constant. Regarding language similarity, our findings indicate that linguistic similarity decreases the likelihood of tie formation, holding everything else constant. In terms of thematic similarity, we estimated the likelihood of tie formation among all possible thematic pairs in the network. The results range from the intuitive increase in likelihood of tie formation in pairs like Conspiracy Theory-Conspiracy Theory groups, or Politics-Politics groups. Nevertheless, our estimates indicate that other possible thematic pairs that include Conspiracy Theory groups see an increase in likelihood of tie formation, such as in the cases of Conspiracy Theory groups paired with Community, Media, Politics or Religion & Spirituality groups, which seems to indicate a sort of spillover effect on the diffusion of problematic information from dedicated Facebook groups to non-dedicated conspiratorial groups. DR ERGM Table 2 presents the estimated ERGM coefficients. Table 2 ERGM coefficients for the DR network In the DR network data geographical co-location indicates a positive and statistically significant effect on the likelihood of tie formation, as well as cultural similarity, which shows a positive and significant effect. In this regard the estimates differ from the MPV network, which could be attributed to the higher population variance in terms of culture, language, geography, etc. In the same vein, it seems that language similarity, would increase the likelihood of tie formation in the network, but our estimate is not statistically significant. Finally, in terms of thematic similarity, the same trend operates on all possible thematic pairs in the network. In that sense, we see the same intuitive increase in likelihood effect in Conspiracy Theory-Conspiracy Theory, and Politics-Politics groups. More interestingly, our estimates for this network also indicate that other thematic pairs in which Conspiracy Theory groups are active see an increase in likelihood of tie formation. In other words, thematic pairs in which Conspiracy Theory groups are paired with Community, Media or Politics groups also see an increase in the likelihood of tie formation in the network (there were no Religion & Spirituality groups in the super-spreader core for the DR network). This finding, reiterating, would indicate a spillover effect on the diffusion of problematic information from dedicated groups to unassuming and vulnerable groups. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Esteban Villa-Turek, Corresponding author; (2) Rod Abhari, Collaborator; (3) Erik C. Nisbet, Collaborator; (4) Yu Xu, Collaborator; (5) Ayse Deniz Lokmanoglu, Collaborator. Authors: Authors: (1) Esteban Villa-Turek, Corresponding author; (2) Rod Abhari, Collaborator; (3) Erik C. Nisbet, Collaborator; (4) Yu Xu, Collaborator; (5) Ayse Deniz Lokmanoglu, Collaborator. Table of Links Abstract Abstract Transnational Network Dynamics of Problematic Information Diffusion Transnational Network Dynamics of Problematic Information Diffusion Theoretical Framework Theoretical Framework Methodology Methodology Statistical Analysis and Results Statistical Analysis and Results Discussion and Limitations Discussion and Limitations Conclusions and Policy Implications, and References Conclusions and Policy Implications, and References Statistical Analysis and Results After analyzing the main brokers in the network qualitatively, the study employed Exponential Random Graph Models (ERGMs) to statistically determine which nodal attributes are significant in explaining the emergence of network ties. ERGMs are particularly well suited for this task, as they allow researchers to statistically assess and predict the structural characteristics of ties in an observed network. This operates by simulating randomly-generated networks that are specified to structurally equate the observed network, allowing to conduct hypothesis tests on the observed network’s attributes (Robins & Lusher, 2013). In other words, ERGMs are useful to compare in this study the relative influence of transnational factors on tie formation in the networks to other non-transnational actor-level factors to contrast their predictive power around the emergence dynamics of the networks (Robins et al., 2007). In fact, ERGMs have been emphasized in the literature as an ideal method for social network analysis in communication studies, mainly because they allow researchers to employ structure, external relationships or attributes as predictors, all while accounting for the interdependent nature of network data (Shumate & Palazzolo, 2010). Therefore, the study estimates two ERGMs on the two networks using the ergm package version 3.9.4, as implemented for the R programing language (Handcock et al., 2018). To estimate ERGMs for both networks, the study makes use of endogenous attributes as mechanisms through which the network is structured, as well as of exogenous attributes of the nodes and of exogenous relations in the network (Contractor et al., 2006). In general, endogenous attributes correspond to those self-organizing factors in a network that are merely structural and do not depend on attributes of nodes or edges, like the number of edges or triangles. Conversely, exogenous attributes are the ones that help explain the formation of ties in a network based on nonstructural attributes, like attributes of nodes and edges, or other relationships between the nodes that exist in other networks, and that could help explain the emergence of ties in the observed network. We hypothesize, as mentioned above, that linguistic similarity, thematic similarity, and cultural and geographic proximity increase the likelihood of tie formation in both networks. If the hypotheses hold, it would be expected to see a higher likelihood in co-sharing activity in the network between groups that share the same language, the same themes, and that are culturally or geographically close to one another, as captured by the SCI (Bailey et al., 2018). Exogenous attributes are employed to estimate the influence of non-structural characteristics of the network on its emergence, like node characteristics or external network relationships. The exogenous nodal attributes to test for linguistic and thematic similarity are given by the hand-coded language and group category labels introduced above. The exogenous relations in the network to account for cultural similarity are modeled using the SCI, which is calculated by taking the number of Facebook connections between two geographic regions and dividing them by the product of Facebook users in both regions, which is then scaled to have a maximum value of 1,000,000,000 (maximum connectedness) and 1 (minimum connectedness), accounting for every possible country-country pair and without having any missing values (Facebook Data For Good Social Connectedness Index Methodology, 2021). This study employs the SCI to account for cultural similarity relations in social networks, especially those that emerge across national borders. As mentioned above, the SCI empirically shows that people across the world tend to form ties with those who are most similar to them, based for instance on sociodemographic characteristics like race, age, religion, education, but also on other “intangible aspects such as attitudes and beliefs (…)”, which can be understood, among others, as cultural in nature (Bailey et al., 2018). To reiterate, the SCI has proven useful to correlate transnational connectedness with historic intranational and international migratory patterns (Bailey et al., 2018), a relationship that might help account for the emergence of ties between seemingly distant (or close) countries in both networks under study. Finally, the nodal attribute to model geographic co-location is given by the hand-coded domain label discussed above. MPV ERGM MPV ERGM MPV ERGM Table 1 presents the estimated ERGM coefficients. Table 1 Table 1 ERGM coefficients for the MPV network ERGM coefficients for the MPV network The interpretation of ERGM coefficients is similar to that of a logistic regression. In that sense, they allow us to assess whether each term increases or decreases the likelihood of tie formation in the networks. In terms of geographical co-location, the results do not indicate a statistically significant effect on the likelihood of tie formation. However, the same cannot be said of cultural similarity, which shows a positive and significant effect, indicating that cultural similarity between groups’ nationalities increases the likelihood of tie formation in the network, holding everything else constant. Regarding language similarity, our findings indicate that linguistic similarity decreases the likelihood of tie formation, holding everything else constant. In terms of thematic similarity, we estimated the likelihood of tie formation among all possible thematic pairs in the network. The results range from the intuitive increase in likelihood of tie formation in pairs like Conspiracy Theory-Conspiracy Theory groups, or Politics-Politics groups. Nevertheless, our estimates indicate that other possible thematic pairs that include Conspiracy Theory groups see an increase in likelihood of tie formation, such as in the cases of Conspiracy Theory groups paired with Community, Media, Politics or Religion & Spirituality groups, which seems to indicate a sort of spillover effect on the diffusion of problematic information from dedicated Facebook groups to non-dedicated conspiratorial groups. DR ERGM DR ERGM Table 2 presents the estimated ERGM coefficients. Table 2 Table 2 ERGM coefficients for the DR network ERGM coefficients for the DR network In the DR network data geographical co-location indicates a positive and statistically significant effect on the likelihood of tie formation, as well as cultural similarity, which shows a positive and significant effect. In this regard the estimates differ from the MPV network, which could be attributed to the higher population variance in terms of culture, language, geography, etc. In the same vein, it seems that language similarity, would increase the likelihood of tie formation in the network, but our estimate is not statistically significant. Finally, in terms of thematic similarity, the same trend operates on all possible thematic pairs in the network. In that sense, we see the same intuitive increase in likelihood effect in Conspiracy Theory-Conspiracy Theory, and Politics-Politics groups. More interestingly, our estimates for this network also indicate that other thematic pairs in which Conspiracy Theory groups are active see an increase in likelihood of tie formation. In other words, thematic pairs in which Conspiracy Theory groups are paired with Community, Media or Politics groups also see an increase in the likelihood of tie formation in the network (there were no Religion & Spirituality groups in the super-spreader core for the DR network). This finding, reiterating, would indicate a spillover effect on the diffusion of problematic information from dedicated groups to unassuming and vulnerable groups. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Graphing the Gossip: Unraveling Social Media Conspiracies

HOLLINGFORD GOSSIPS.

Rainbow Valley by L. M. Montgomery, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . MORE GOSSIP CHAPTER XV. MORE GOSSIP On the evening after Mrs. Myra Murray of the over-harbour section had been buried Miss Cornelia and Mary Vance came up to Ingleside. There were several things concerning which Miss Cornelia wished to unburden her soul. The funeral had to be all talked over, of course. Susan and Miss Cornelia thrashed this out between them; Anne took no part or delight in such goulish conversations. She sat a little apart and watched the autumnal flame of dahlias in the garden, and the dreaming, glamorous harbour of the September sunset. Mary Vance sat beside her, knitting meekly. Mary’s heart was down in the Rainbow Valley, whence came sweet, distance-softened sounds of children’s laughter, but her fingers were under Miss Cornelia’s eye. She had to knit so many rounds of her stocking before she might go to the valley. Mary knit and held her tongue, but used her ears. “I never saw a nicer looking corpse,” said Miss Cornelia judicially. “Myra Murray was always a pretty woman—she was a Corey from Lowbridge and the Coreys were noted for their good looks.” “I said to the corpse as I passed it, ‘poor woman. I hope you are as happy as you look.’” sighed Susan. “She had not changed much. That dress she wore was the black satin she got for her daughter’s wedding fourteen years ago. Her Aunt told her then to keep it for her funeral, but Myra laughed and said, ‘I may wear it to my funeral, Aunty, but I will have a good time out of it first.’ And I may say she did. Myra Murray was not a woman to attend her own funeral before she died. Many a time afterwards when I saw her enjoying herself out in company I thought to myself, ‘You are a handsome woman, Myra Murray, and that dress becomes you, but it will likely be your shroud at last.’ And you see my words have come true, Mrs. Marshall Elliott.” Susan sighed again heavily. She was enjoying herself hugely. A funeral was really a delightful subject of conversation. “I always liked to meet Myra,” said Miss Cornelia. “She was always so gay and cheerful—she made you feel better just by her handshake. Myra always made the best of things.” “That is true,” asserted Susan. “Her sister-in-law told me that when the doctor told her at last that he could do nothing for her and she would never rise from that bed again, Myra said quite cheerfully, ‘Well, if that is so, I’m thankful the preserving is all done, and I will not have to face the fall house-cleaning. I always liked house-cleaning in spring,’ she says, ‘but I always hated it in the fall. I will get clear of it this year, thank goodness.’ There are people who would call that levity, Mrs. Marshall Elliott, and I think her sister-in-law was a little ashamed of it. She said perhaps her sickness had made Myra a little light-headed. But I said, ‘No, Mrs. Murray, do not worry over it. It was just Myra’s way of looking at the bright side.’” “Her sister Luella was just the opposite,” said Miss Cornelia. “There was no bright side for Luella—there was just black and shades of gray. For years she used always to be declaring she was going to die in a week or so. ‘I won’t be here to burden you long,’ she would tell her family with a groan. And if any of them ventured to talk about their little future plans she’d groan also and say, ‘Ah, I won’t be here then.’ When I went to see her I always agreed with her and it made her so mad that she was always quite a lot better for several days afterwards. She has better health now but no more cheerfulness. Myra was so different. She was always doing or saying something to make some one feel good. Perhaps the men they married had something to do with it. Luella’s man was a Tartar, believe me, while Jim Murray was decent, as men go. He looked heart-broken to-day. It isn’t often I feel sorry for a man at his wife’s funeral, but I did feel for Jim Murray.” “No wonder he looked sad. He will not get a wife like Myra again in a hurry,” said Susan. “Maybe he will not try, since his children are all grown up and Mirabel is able to keep house. But there is no predicting what a widower may or may not do and I, for one, will not try.” “We’ll miss Myra terrible in church,” said Miss Cornelia. “She was such a worker. Nothing ever stumped her. If she couldn’t get over a difficulty she’d get around it, and if she couldn’t get around it she’d pretend it wasn’t there—and generally it wasn’t. ‘I’ll keep a stiff upper lip to my journey’s end,’ said she to me once. Well, she has ended her journey.” “Do you think so?” asked Anne suddenly, coming back from dreamland. “I can’t picture her journey as being ended. Can you think of her sitting down and folding her hands—that eager, asking spirit of hers, with its fine adventurous outlook? No, I think in death she just opened a gate and went through—on—on—to new, shining adventures.” “Maybe—maybe,” assented Miss Cornelia. “Do you know, Anne dearie, I never was much taken with this everlasting rest doctrine myself—though I hope it isn’t heresy to say so. I want to bustle round in heaven the same as here. And I hope there’ll be a celestial substitute for pies and doughnuts—something that has to be MADE. Of course, one does get awful tired at times—and the older you are the tireder you get. But the very tiredest could get rested in something short of eternity, you’d think—except, perhaps, a lazy man.” “When I meet Myra Murray again,” said Anne, “I want to see her coming towards me, brisk and laughing, just as she always did here.” “Oh, Mrs. Dr. dear,” said Susan, in a shocked tone, “you surely do not think that Myra will be laughing in the world to come?” “Why not, Susan? Do you think we will be crying there?” “No, no, Mrs. Dr. dear, do not misunderstand me. I do not think we shall be either crying or laughing.” “What then?” “Well,” said Susan, driven to it, “it is my opinion, Mrs. Dr. dear, that we shall just look solemn and holy.” “And do you really think, Susan,” said Anne, looking solemn enough, “that either Myra Murray or I could look solemn and holy all the time—all the time, Susan?” “Well,” admitted Susan reluctantly, “I might go so far as to say that you both would have to smile now and again, but I can never admit that there will be laughing in heaven. The idea seems really irreverent, Mrs. Dr. dear.” “Well, to come back to earth,” said Miss Cornelia, “who can we get to take Myra’s class in Sunday School? Julia Clow has been teaching it since Myra took ill, but she’s going to town for the winter and we’ll have to get somebody else.” “I heard that Mrs. Laurie Jamieson wanted it,” said Anne. “The Jamiesons have come to church very regularly since they moved to the Glen from Lowbridge.” “New brooms!” said Miss Cornelia dubiously. “Wait till they’ve gone regularly for a year.” “You cannot depend on Mrs. Jamieson a bit, Mrs. Dr. dear,” said Susan solemnly. “She died once and when they were measuring her for her coffin, after laying her out just beautiful, did she not go and come back to life! Now, Mrs. Dr. dear, you know you cannot depend on a woman like that.” “She might turn Methodist at any moment,” said Miss Cornelia. “They tell me they went to the Methodist Church at Lowbridge quite as often as to the Presbyterian. I haven’t caught them at it here yet, but I would not approve of taking Mrs. Jamieson into the Sunday School. Yet we must not offend them. We are losing too many people, by death or bad temper. Mrs. Alec Davis has left the church, no one knows why. She told the managers that she would never pay another cent to Mr. Meredith’s salary. Of course, most people say that the children offended her, but somehow I don’t think so. I tried to pump Faith, but all I could get out of her was that Mrs. Davis had come, seemingly in high good humour, to see her father, and had left in an awful rage, calling them all ‘varmints!’” “Varmints, indeed!” said Susan furiously. “Does Mrs. Alec Davis forget that her uncle on her mother’s side was suspected of poisoning his wife? Not that it was ever proved, Mrs. Dr. dear, and it does not do to believe all you hear. But if I had an uncle whose wife died without any satisfactory reason, I would not go about the country calling innocent children varmints.” “The point is,” said Miss Cornelia, “that Mrs. Davis paid a large subscription, and how its loss is going to be made up is a problem. And if she turns the other Douglases against Mr. Meredith, as she will certainly try to do, he will just have to go.” “I do not think Mrs. Alec Davis is very well liked by the rest of the clan,” said Susan. “It is not likely she will be able to influence them.” “But those Douglases all hang together so. If you touch one, you touch all. We can’t do without them, so much is certain. They pay half the salary. They are not mean, whatever else may be said of them. Norman Douglas used to give a hundred a year long ago before he left.” “What did he leave for?” asked Anne. “He declared a member of the session cheated him in a cow deal. He hasn’t come to church for twenty years. His wife used to come regular while she was alive, poor thing, but he never would let her pay anything, except one red cent every Sunday. She felt dreadfully humiliated. I don’t know that he was any too good a husband to her, though she was never heard to complain. But she always had a cowed look. Norman Douglas didn’t get the woman he wanted thirty years ago and the Douglases never liked to put up with second best.” “Who was the woman he did want.” “Ellen West. They weren’t engaged exactly, I believe, but they went about together for two years. And then they just broke off—nobody ever knew why. Just some silly quarrel, I suppose. And Norman went and married Hester Reese before his temper had time to cool—married her just to spite Ellen, I haven’t a doubt. So like a man! Hester was a nice little thing, but she never had much spirit and he broke what little she had. She was too meek for Norman. He needed a woman who could stand up to him. Ellen would have kept him in fine order and he would have liked her all the better for it. He despised Hester, that is the truth, just because she always gave in to him. I used to hear him say many a time, long ago when he was a young fellow ‘Give me a spunky woman—spunk for me every time.’ And then he went and married a girl who couldn’t say boo to a goose—man-like. That family of Reeses were just vegetables. They went through the motions of living, but they didn’t live.” “Russell Reese used his first wife’s wedding-ring to marry his second,” said Susan reminiscently. “That was too economical in my opinion, Mrs. Dr. dear. And his brother John has his own tombstone put up in the over-harbour graveyard, with everything on it but the date of death, and he goes and looks at it every Sunday. Most folks would not consider that much fun, but it is plain he does. People do have such different ideas of enjoyment. As for Norman Douglas, he is a perfect heathen. When the last minister asked him why he never went to church he said ‘Too many ugly women there, parson—too many ugly women!’ I should like to go to such a man, Mrs. Dr. dear, and say to him solemnly, ‘There is a hell!’” “Oh, Norman doesn’t believe there is such a place,” said Miss Cornelia. “I hope he’ll find out his mistake when he comes to die. There, Mary, you’ve knit your three inches and you can go and play with the children for half an hour.” Mary needed no second bidding. She flew to Rainbow Valley with a heart as light as her heels, and in the course of conversation told Faith Meredith all about Mrs. Alec Davis. “And Mrs. Elliott says that she’ll turn all the Douglases against your father and then he’ll have to leave the Glen because his salary won’t be paid,” concluded Mary. “I don’t know what is to be done, honest to goodness. If only old Norman Douglas would come back to church and pay, it wouldn’t be so bad. But he won’t—and the Douglases will leave—and you all will have to go.” Faith carried a heavy heart to bed with her that night. The thought of leaving the Glen was unbearable. Nowhere else in the world were there such chums as the Blythes. Her little heart had been wrung when they had left Maywater—she had shed many bitter tears when she parted with Maywater chums and the old manse there where her mother had lived and died. She could not contemplate calmly the thought of such another and harder wrench. She couldn’t leave Glen St. Mary and dear Rainbow Valley and that delicious graveyard. “It’s awful to be minister’s family,” groaned Faith into her pillow. “Just as soon as you get fond of a place you are torn up by the roots. I’ll never, never, never marry a minister, no matter how nice he is.” Faith sat up in bed and looked out of the little vine-hung window. The night was very still, the silence broken only by Una’s soft breathing. Faith felt terribly alone in the world. She could see Glen St. Mary lying under the starry blue meadows of the autumn night. Over the valley a light shone from the girls’ room at Ingleside, and another from Walter’s room. Faith wondered if poor Walter had toothache again. Then she sighed, with a little passing sigh of envy of Nan and Di. They had a mother and a settled home—they were not at the mercy of people who got angry without any reason and called you a varmint. Away beyond the Glen, amid fields that were very quiet with sleep, another light was burning. Faith knew it shone in the house where Norman Douglas lived. He was reputed to sit up all hours of the night reading. Mary had said if he could only be induced to return to the church all would be well. And why not? Faith looked at a big, low star hanging over the tall, pointed spruce at the gate of the Methodist Church and had an inspiration. She knew what ought to be done and she, Faith Meredith, would do it. She would make everything right. With a sigh of satisfaction, she turned from the lonely, dark world and cuddled down beside Una. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. L. M. Montgomery (2004). Rainbow Valley. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/5343/pg5343-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html.

MORE GOSSIP

Rainbow Valley by L. M. Montgomery, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . SHEER GOSSIP CHAPTER II. SHEER GOSSIP “Where are the other children?” asked Miss Cornelia, when the first greetings—cordial on her side, rapturous on Anne’s, and dignified on Susan’s—were over. “Shirley is in bed and Jem and Walter and the twins are down in their beloved Rainbow Valley,” said Anne. “They just came home this afternoon, you know, and they could hardly wait until supper was over before rushing down to the valley. They love it above every spot on earth. Even the maple grove doesn’t rival it in their affections.” “I am afraid they love it too well,” said Susan gloomily. “Little Jem said once he would rather go to Rainbow Valley than to heaven when he died, and that was not a proper remark.” “I suppose they had a great time in Avonlea?” said Miss Cornelia. “Enormous. Marilla does spoil them terribly. Jem, in particular, can do no wrong in her eyes.” “Miss Cuthbert must be an old lady now,” said Miss Cornelia, getting out her knitting, so that she could hold her own with Susan. Miss Cornelia held that the woman whose hands were employed always had the advantage over the woman whose hands were not. “Marilla is eighty-five,” said Anne with a sigh. “Her hair is snow-white. But, strange to say, her eyesight is better than it was when she was sixty.” “Well, dearie, I’m real glad you’re all back. I’ve been dreadful lonesome. But we haven’t been dull in the Glen, believe me. There hasn’t been such an exciting spring in my time, as far as church matters go. We’ve got settled with a minister at last, Anne dearie.” “The Reverend John Knox Meredith, Mrs. Dr. dear,” said Susan, resolved not to let Miss Cornelia tell all the news. “Is he nice?” asked Anne interestedly. Miss Cornelia sighed and Susan groaned. “Yes, he’s nice enough if that were all,” said the former. “He is very nice—and very learned—and very spiritual. But, oh Anne dearie, he has no common sense! “How was it you called him, then?” “Well, there’s no doubt he is by far the best preacher we ever had in Glen St. Mary church,” said Miss Cornelia, veering a tack or two. “I suppose it is because he is so moony and absent-minded that he never got a town call. His trial sermon was simply wonderful, believe me. Every one went mad about it—and his looks.” “He is very comely, Mrs. Dr. dear, and when all is said and done, I do like to see a well-looking man in the pulpit,” broke in Susan, thinking it was time she asserted herself again. “Besides,” said Miss Cornelia, “we were anxious to get settled. And Mr. Meredith was the first candidate we were all agreed on. Somebody had some objection to all the others. There was some talk of calling Mr. Folsom. He was a good preacher, too, but somehow people didn’t care for his appearance. He was too dark and sleek.” “He looked exactly like a great black tomcat, that he did, Mrs. Dr. dear,” said Susan. “I never could abide such a man in the pulpit every Sunday.” “Then Mr. Rogers came and he was like a chip in porridge—neither harm nor good,” resumed Miss Cornelia. “But if he had preached like Peter and Paul it would have profited him nothing, for that was the day old Caleb Ramsay’s sheep strayed into church and gave a loud ‘ba-a-a’ just as he announced his text. Everybody laughed, and poor Rogers had no chance after that. Some thought we ought to call Mr. Stewart, because he was so well educated. He could read the New Testament in five languages.” “But I do not think he was any surer than other men of getting to heaven because of that,” interjected Susan. “Most of us didn’t like his delivery,” said Miss Cornelia, ignoring Susan. “He talked in grunts, so to speak. And Mr. Arnett couldn’t preach at all. And he picked about the worst candidating text there is in the Bible—‘Curse ye Meroz.’” “Whenever he got stuck for an idea, he would bang the Bible and shout very bitterly, ‘Curse ye Meroz.’ Poor Meroz got thoroughly cursed that day, whoever he was, Mrs. Dr. dear,” said Susan. “The minister who is candidating can’t be too careful what text he chooses,” said Miss Cornelia solemnly. “I believe Mr. Pierson would have got the call if he had picked a different text. But when he announced ‘I will lift my eyes to the hills’ he was done for. Every one grinned, for every one knew that those two Hill girls from the Harbour Head have been setting their caps for every single minister who came to the Glen for the last fifteen years. And Mr. Newman had too large a family.” “He stayed with my brother-in-law, James Clow,” said Susan. “‘How many children have you got?’ I asked him. ‘Nine boys and a sister for each of them,’ he said. ‘Eighteen!’ said I. ‘Dear me, what a family!’ And then he laughed and laughed. But I do not know why, Mrs. Dr. dear, and I am certain that eighteen children would be too many for any manse.” “He had only ten children, Susan,” explained Miss Cornelia, with contemptuous patience. “And ten good children would not be much worse for the manse and congregation than the four who are there now. Though I wouldn’t say, Anne dearie, that they are so bad, either. I like them—everybody likes them. It’s impossible to help liking them. They would be real nice little souls if there was anyone to look after their manners and teach them what is right and proper. For instance, at school the teacher says they are model children. But at home they simply run wild.” “What about Mrs. Meredith?” asked Anne. “There’s no Mrs. Meredith. That is just the trouble. Mr. Meredith is a widower. His wife died four years ago. If we had known that I don’t suppose we would have called him, for a widower is even worse in a congregation than a single man. But he was heard to speak of his children and we all supposed there was a mother, too. And when they came there was nobody but old Aunt Martha, as they call her. She’s a cousin of Mr. Meredith’s mother, I believe, and he took her in to save her from the poorhouse. She is seventy-five years old, half blind, and very deaf and very cranky.” “And a very poor cook, Mrs. Dr. dear.” “The worst possible manager for a manse,” said Miss Cornelia bitterly. “Mr. Meredith won’t get any other housekeeper because he says it would hurt Aunt Martha’s feelings. Anne dearie, believe me, the state of that manse is something terrible. Everything is thick with dust and nothing is ever in its place. And we had painted and papered it all so nice before they came.” “There are four children, you say?” asked Anne, beginning to mother them already in her heart. “Yes. They run up just like the steps of a stair. Gerald’s the oldest. He’s twelve and they call him Jerry. He’s a clever boy. Faith is eleven. She is a regular tomboy but pretty as a picture, I must say.” “She looks like an angel but she is a holy terror for mischief, Mrs. Dr. dear,” said Susan solemnly. “I was at the manse one night last week and Mrs. James Millison was there, too. She had brought them up a dozen eggs and a little pail of milk—a very little pail, Mrs. Dr. dear. Faith took them and whisked down the cellar with them. Near the bottom of the stairs she caught her toe and fell the rest of the way, milk and eggs and all. You can imagine the result, Mrs. Dr. dear. But that child came up laughing. ‘I don’t know whether I’m myself or a custard pie,’ she said. And Mrs. James Millison was very angry. She said she would never take another thing to the manse if it was to be wasted and destroyed in that fashion.” “Maria Millison never hurt herself taking things to the manse,” sniffed Miss Cornelia. “She just took them that night as an excuse for curiosity. But poor Faith is always getting into scrapes. She is so heedless and impulsive.” “Just like me. I’m going to like your Faith,” said Anne decidedly. “She is full of spunk—and I do like spunk, Mrs. Dr. dear,” admitted Susan. “There’s something taking about her,” conceded Miss Cornelia. “You never see her but she’s laughing, and somehow it always makes you want to laugh too. She can’t even keep a straight face in church. Una is ten—she’s a sweet little thing—not pretty, but sweet. And Thomas Carlyle is nine. They call him Carl, and he has a regular mania for collecting toads and bugs and frogs and bringing them into the house.” “I suppose he was responsible for the dead rat that was lying on a chair in the parlour the afternoon Mrs. Grant called. It gave her a turn,” said Susan, “and I do not wonder, for manse parlours are no places for dead rats. To be sure it may have been the cat who left it, there. He is as full of the old Nick as he can be stuffed, Mrs. Dr. dear. A manse cat should at least look respectable, in my opinion, whatever he really is. But I never saw such a rakish-looking beast. And he walks along the ridgepole of the manse almost every evening at sunset, Mrs. Dr. dear, and waves his tail, and that is not becoming.” “The worst of it is, they are never decently dressed,” sighed Miss Cornelia. “And since the snow went they go to school barefooted. Now, you know Anne dearie, that isn’t the right thing for manse children—especially when the Methodist minister’s little girl always wears such nice buttoned boots. And I do wish they wouldn’t play in the old Methodist graveyard.” “It’s very tempting, when it’s right beside the manse,” said Anne. “I’ve always thought graveyards must be delightful places to play in.” “Oh, no, you did not, Mrs. Dr. dear,” said loyal Susan, determined to protect Anne from herself. “You have too much good sense and decorum.” “Why did they ever build that manse beside the graveyard in the first place?” asked Anne. “Their lawn is so small there is no place for them to play except in the graveyard.” “It was a mistake,” admitted Miss Cornelia. “But they got the lot cheap. And no other manse children ever thought of playing there. Mr. Meredith shouldn’t allow it. But he has always got his nose buried in a book, when he is home. He reads and reads, or walks about in his study in a day-dream. So far he hasn’t forgotten to be in church on Sundays, but twice he has forgotten about the prayer-meeting and one of the elders had to go over to the manse and remind him. And he forgot about Fanny Cooper’s wedding. They rang him up on the ‘phone and then he rushed right over, just as he was, carpet slippers and all. One wouldn’t mind if the Methodists didn’t laugh so about it. But there’s one comfort—they can’t criticize his sermons. He wakes up when he’s in the pulpit, believe me. And the Methodist minister can’t preach at all—so they tell me. I have never heard him, thank goodness.” Miss Cornelia’s scorn of men had abated somewhat since her marriage, but her scorn of Methodists remained untinged of charity. Susan smiled slyly. “They do say, Mrs. Marshall Elliott, that the Methodists and Presbyterians are talking of uniting,” she said. “Well, all I hope is that I’ll be under the sod if that ever comes to pass,” retorted Miss Cornelia. “I shall never have truck or trade with Methodists, and Mr. Meredith will find that he’d better steer clear of them, too. He is entirely too sociable with them, believe me. Why, he went to the Jacob Drews’ silver-wedding supper and got into a nice scrape as a result.” “What was it?” “Mrs. Drew asked him to carve the roast goose—for Jacob Drew never did or could carve. Well, Mr. Meredith tackled it, and in the process he knocked it clean off the platter into Mrs. Reese’s lap, who was sitting next him. And he just said dreamily. ‘Mrs. Reese, will you kindly return me that goose?’ Mrs. Reese ‘returned’ it, as meek as Moses, but she must have been furious, for she had on her new silk dress. The worst of it is, she was a Methodist.” “But I think that is better than if she was a Presbyterian,” interjected Susan. “If she had been a Presbyterian she would mostly likely have left the church and we cannot afford to lose our members. And Mrs. Reese is not liked in her own church, because she gives herself such great airs, so that the Methodists would be rather pleased that Mr. Meredith spoiled her dress.” “The point is, he made himself ridiculous, and I, for one, do not like to see my minister made ridiculous in the eyes of the Methodists,” said Miss Cornelia stiffly. “If he had had a wife it would not have happened.” “I do not see if he had a dozen wives how they could have prevented Mrs. Drew from using up her tough old gander for the wedding-feast,” said Susan stubbornly. “They say that was her husband’s doing,” said Miss Cornelia. “Jacob Drew is a conceited, stingy, domineering creature.” “And they do say he and his wife detest each other—which does not seem to me the proper way for married folks to get along. But then, of course, I have had no experience along that line,” said Susan, tossing her head. “And I am not one to blame everything on the men. Mrs. Drew is mean enough herself. They say that the only thing she was ever known to give away was a crock of butter made out of cream a rat had fell into. She contributed it to a church social. Nobody found out about the rat until afterwards.” “Fortunately, all the people the Merediths have offended so far are Methodists,” said Miss Cornelia. “That Jerry went to the Methodist prayer-meeting one night about a fortnight ago and sat beside old William Marsh who got up as usual and testified with fearful groans. ‘Do you feel any better now?’ whispered Jerry when William sat down. Poor Jerry meant to be sympathetic, but Mr. Marsh thought he was impertinent and is furious at him. Of course, Jerry had no business to be in a Methodist prayer-meeting at all. But they go where they like.” “I hope they will not offend Mrs. Alec Davis of the Harbour Head,” said Susan. “She is a very touchy woman, I understand, but she is very well off and pays the most of any one to the salary. I have heard that she says the Merediths are the worst brought up children she ever saw.” “Every word you say convinces me more and more that the Merediths belong to the race that knows Joseph,” said Mistress Anne decidedly. “When all is said and done, they do,” admitted Miss Cornelia. “And that balances everything. Anyway, we’ve got them now and we must just do the best we can by them and stick up for them to the Methodists. Well, I suppose I must be getting down harbour. Marshall will soon be home—he went over-harbour to-day—and wanting his super, man-like. I’m sorry I haven’t seen the other children. And where’s the doctor?” “Up at the Harbour Head. We’ve only been home three days and in that time he has spent three hours in his own bed and eaten two meals in his own house.” “Well, everybody who has been sick for the last six weeks has been waiting for him to come home—and I don’t blame them. When that over-harbour doctor married the undertaker’s daughter at Lowbridge people felt suspicious of him. It didn’t look well. You and the doctor must come down soon and tell us all about your trip. I suppose you’ve had a splendid time.” “We had,” agreed Anne. “It was the fulfilment of years of dreams. The old world is very lovely and very wonderful. But we have come back very well satisfied with our own land. Canada is the finest country in the world, Miss Cornelia.” “Nobody ever doubted that,” said Miss Cornelia, complacently. “And old P.E.I. is the loveliest province in it and Four Winds the loveliest spot in P.E.I.,” laughed Anne, looking adoringly out over the sunset splendour of glen and harbour and gulf. She waved her hand at it. “I saw nothing more beautiful than that in Europe, Miss Cornelia. Must you go? The children will be sorry to have missed you.” “They must come and see me soon. Tell them the doughnut jar is always full.” “Oh, at supper they were planning a descent on you. They’ll go soon; but they must settle down to school again now. And the twins are going to take music lessons.” “Not from the Methodist minister’s wife, I hope?” said Miss Cornelia anxiously. “No—from Rosemary West. I was up last evening to arrange it with her. What a pretty girl she is!” “Rosemary holds her own well. She isn’t as young as she once was.” “I thought her very charming. I’ve never had any real acquaintance with her, you know. Their house is so out of the way, and I’ve seldom ever seen her except at church.” “People always have liked Rosemary West, though they don’t understand her,” said Miss Cornelia, quite unconscious of the high tribute she was paying to Rosemary’s charm. “Ellen has always kept her down, so to speak. She has tyrannized over her, and yet she has always indulged her in a good many ways. Rosemary was engaged once, you know—to young Martin Crawford. His ship was wrecked on the Magdalens and all the crew were drowned. Rosemary was just a child—only seventeen. But she was never the same afterwards. She and Ellen have stayed very close at home since their mother’s death. They don’t often get to their own church at Lowbridge and I understand Ellen doesn’t approve of going too often to a Presbyterian church. To the Methodist she never goes, I’ll say that much for her. That family of Wests have always been strong Episcopalians. Rosemary and Ellen are pretty well off. Rosemary doesn’t really need to give music lessons. She does it because she likes to. They are distantly related to Leslie, you know. Are the Fords coming to the harbour this summer?” “No. They are going on a trip to Japan and will probably be away for a year. Owen’s new novel is to have a Japanese setting. This will be the first summer that the dear old House of Dreams will be empty since we left it.” “I should think Owen Ford might find enough to write about in Canada without dragging his wife and his innocent children off to a heathen country like Japan,” grumbled Miss Cornelia. “The Life Book was the best book he’s ever written and he got the material for that right here in Four Winds.” “Captain Jim gave him the most of that, you know. And he collected it all over the world. But Owen’s books are all delightful, I think.” “Oh, they’re well enough as far as they go. I make it a point to read every one he writes, though I’ve always held, Anne dearie, that reading novels is a sinful waste of time. I shall write and tell him my opinion of this Japanese business, believe me. Does he want Kenneth and Persis to be converted into pagans?” With which unanswerable conundrum Miss Cornelia took her departure. Susan proceeded to put Rilla in bed and Anne sat on the veranda steps under the early stars and dreamed her incorrigible dreams and learned all over again for the hundredth happy time what a moonrise splendour and sheen could be on Four Winds Harbour. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. L. M. Montgomery (2004). Rainbow Valley. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/5343/pg5343-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html.

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HackerNoon Reporter: Please tell us briefly about your  background. Hi, my background is in neuroscience from Johns Hopkins University. I had a career that focused on areas of neuroscience, clinical research, and computational neuroscience before I moved over into startup after founding my own company. I also had a tangent career with acting and was a main cast member of the television show Gossip Girl. What's your startup called? And in a sentence or two, what does it do? We are called MyMarkit. We are the world’s first fintech blockchain marketplace, and it begets a whole new way to shop. Our marketplace is built ground up around ledgers that are mined from transactions between users for products in a free market (marketplace), which also creates a truly digital currency for the first time backed to the commodities. Our tech automates traditional overhead fulfillment/COGS and yields 70-80% gross margins at scale. It also means that buyers/vendors keep 100% of what they sell on our platform, while buyers can get the lowest prices possible anywhere. Our automation tech also allows for 100% infallible authentication and data analytics/tracking for the first time for the lifetime of the product. We are starting in the resale luxury market space before moving on to retail verticals. Welcome to a new way to shop! What is the origin story? I came across the idea for a lossless transaction after working on fully sustainable frameworks for a non-profit. I thought, why is it that the moment I purchase something, I will never get anywhere near the price that I just paid for it? Is this item no longer worth anything and the currency I just used to purchase it is worth everything? I realized, that’s not the case. The currency represents the value of the product purchased. From there, we looked at the fundamentals of blockchain and crypto in order to understand currency at its inception (whether it’s fiat, digital, or otherwise). We realized that a free market can translate to a marketplace where transactions between users can mine a currency that represents and is backed to the commodity to begin with. And thus, a lossless transaction and a true digital currency was born. It’s like having real world NFTs for physical goods for the first time. What do you love about your team, and why are you the ones to solve this problem? Our team is made of experts in every area of commerce, blockchain, marketplaces, fintech, and crypto/digital currency. We are a total of over 45 people strong, and we’ve worked over years to build our tech stack from the ground up to scale. I think the most interesting thing is that we were not all so myopic in thinking about how digital currency/blockchain is currently utilized. We had to go to its roots to find a way to use it that neutralized all currencies and brought it into the real world with true backing/worth. If you weren’t building your startup, what would you be doing? I would probably be in some other area of creation or helping others to create. The common theme of my life is to push boundaries and to do things that seem impossible in order to come up with solutions that should exist… but just don’t yet. At the moment, how do you measure success? What are your core metrics? Our success is measured by the market demand and the users. We know our product is something that consumers have asked for but have no alternatives currently to use. We lose so much money in how we purchase. We have so much volatility in how we use currency. We wanted to marry fintech, payment gateways, marketplaces, and blockchain all under one roof. And the most important metric is how happy our customers are with what we’ve created. They say that this is something that should have existed long ago. And for the first time, they don’t have to pay fees or lose out on commissions for selling their stuff. Buyers are so happy that they can get all of the cost savings by getting the lowest price possible on our platform. That is the most successful and most rewarding feedback. What’s most exciting about your traction to date? We have been in stealth mode to build out our technology, so we are just coming out of stealth. The pre-launch user sign up was over our expectations. We’ve onboarded executives from some of the biggest competitors in this space. We’ve had a phenomenal requests to white label our technology and to provide our tech solutions for other ecommerce or marketplace companies. We’ve also had developments in the financial services space to roll out digital wallets, debut our coin, and other exciting offerings. What technologies are you currently most excited about, and most worried about? And why? I’m most excited to see the future developments of digital currencies from countries. I think it’s a game-changing addition to the crypto world in that more regulations will soon arise, for better or worse. I am excited to see how blockchain solutions will be used for everyday use cases instead of more SAAS-focused enterprise functions. Overall, the future of blockchain with regards to financial services and various related spaces will be the one to look out for. What drew you to get published on HackerNoon? What do you like most about our platform? We were nominated for Startup Of The Year, so we wanted to participate in the nominations. We are just going out for press now with our launch, so we appreciate that there is a community of tech-related news sourced by amazing writers to find the latest innovations for readers. What advice would you give to the 21-year-old version of yourself? Get over the concept of fear because on the other side of it is freedom. My biggest regrets were from doing things the expected way because certain ideas or innovations were too “ambitious” for the current market. I believe that’s how dynamic change happens - we have to get out of the mindset of what’s done already to do things that are truly novel and pioneering. What is something surprising you've learned this year that your contemporaries would benefit from knowing? LVMH just paved the way for retail brands to adopt blockchain authentication technology. So, it’s a matter of time before we will be using more variations of blockchain in everyday use cases. MyMarkit Inc. is nominated as a . Go vote! startup of the year in Playa Vista

From Gossip Girl Cast To Building A Blockchain Marketplace: Nan Zhang, MyMarkit Inc. CEO

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