Nigerian filmmaker CJ Obasi’s Mami Wata is delicately poised on contradictions and conflicts: between tradition and modernity, faith and disbelief, trust and incredulity and myth and reality.

Cinema without borders: Mami Wata- Ideological conflict

Robert Courts urged the Turkish President to remember his commitments to "the most successful military alliance in history".

Erdogan's incendiary Hamas remarks a 'real concern' for NATO allies as tension mounts

In the majority decision in Dobbs v. Jackson Women’s Health Organization, which overturned the federal right to abortion, Justice Samuel Alito purported that the electoral and political process are a sufficient antidote to the court’s stripping away of privacy rights.

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Discussions during the Synod, especially in the small circles, were often focused on the centrality of Jesus Christ and the importance of episcopal collegiality.

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In the relationship between nations, trade plays a significant role. Central to the ties between New Delhi and Doha is LNG, the fuel on which India is critically dependent — and of which Qatar is the predominant supplier.

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looming economic recession, broken supply chains, food and energy insecurity this calls for closer cooperation with the SCO. In this context, the centrality of the interests of Central Asian States plays a key role," said Jaishankar

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The centrality of national interest remains paramount amid the ongoing flux where the salience of national security is continuously increasing in the international system, said the Army Chief.

Land warfare will remain important: Army Chief on lessons from Ukraine

Speaking at the curtain raiser event of Chanakya Defence Dialogue in New Delhi on October 26, Indian Army Chief General Manoj Pande spoke about the profound lessons that India has learnt from the prolonged Russia-Ukraine war.

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He also mentioned that “We are seeing unprecedented changes in the world. We are also seeing the centrality of national interest in global affairs. National security importance is growing in international affairs”.

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The Association of Southeast Asian Nations (ASEAN)’s centrality in maintaining peace and prosperity in the region was highlighted at the 15th East Sea International Conference that opened in Ho Chi Minh City on October 25.

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Centrality

centrality

The future looks bright for the world of blockchain. According to , the industry will generate an annual business value of over $175 billion by 2025, reaching $3 trillion by 2030. However, while these figures are impressive, there’s much more to the story. Gartner As businesses continue to leverage the functions of decentralized applications (DApps), obstacles remain for those building the underlying infrastructure. In reality, launching a blockchain project is often a daunting task, and the stakes are higher than ever as the hype cycle winds down. According to , investment in blockchain ventures is expected to hit $1.6 billion across 454 deals this year, a steep drop from the $4.1 billion invested in 2018. CB Insights But what does all this mean? In short, incumbent tech companies, blockchain-centric and otherwise, continue to capture and generate the majority of ecosystem value. All the while, newcomers face an uphill battle. For these new market entrants, fierce competition, long-standing incumbent relationships, and big budgets remain a hindrance - but there is a path forward. In response to the obstacles facing new DApp ventures, platforms like Centrality are establishing start-up communities that aim to bolster the success of new DApp ventures. BUIDLing a Community By sharing resources; tech, human, or otherwise, DApp projects on the Centrality platform naturally increase their chances of success - a welcome boost in light of market data. According to a 2015 report from , the average app loses 77% of its daily active users (DAUs) within three days of the initial install. Within 30 days, the app loses 90% of DAUs, and within 90 days, it’s over 95%. In essence, most apps lose all of their DAUs within three months of launching. Quettra When considering the implications of this trend across the decentralized ecosystem, the drop-off could be even more profound in the absence of a vast user base. As such, the Centrality platform offers immense value to community members looking for a safe place to experiment, innovate, and collaborate. Rebooting the Startup Lifecycle The industry’s recent experience with initial coin offerings (ICOs) highlights the shortcomings of existing start-up models. Once the engine of DApp funding, direct venture capital has eclipsed ICO funding for the . But what does this tell us? last four quarters In general, it speaks to the risks associated with overzealous DApp development in the absence of proven concepts and adequate resources. On the Centrality platform, these risks are mitigated through a human-centric approach to each stage of the start-up life cycle. By allowing users to interact with a range of DApps within the same ecosystem, every project benefits from the consolidation of resources - a welcome change from prominent industry fragmentation today. By leveraging this collective scale, DApp developers using the Centrality platform are more likely to overcome many of the incumbent startup hurdles. In turn, the trajectory of the existing startup lifecycle can be amended. When considering the primary obstacles to blockchain adoption, Centrality functionality is further galvanized. The Hype Cycle As mentioned, many DApp projects have over-promised and under-delivered. And while blockchain remains a transformative technology, there are limitations to what it can do. In a survey conducted by the , respondents expected a 24% return on investment (ROI) on their new blockchain projects. However, the reality is much lower at an average return of 10%. World Economic Forum Although the still refers to blockchain market segments, it's apparent that the initial hype has passed. The Centrality platform responds to this trend by providing an environment where projects work on value-added projects rooted in reality. 2019 Gartner Hype Cycle for Emerging Tech Stakeholder Buy-In Moving a blockchain project from proof of concept to production is impossible without stakeholder buy-in. The Centrality platform helps reduce the friction associated with this process by offering a more affordable, risk-mitigated approach to project development. Instead of making massive capital investments to start from scratch, projects can start using pre-built components out of the gate. Capturing Ecosystem Value At this point in blockchain market maturation, it's apparent that working with others is critical for successful blockchain implementation. To ensure consistent standards see application across the ecosystem, organizations must work collaboratively to overcome challenges. This approach reflects the intent of Centrality's platform, where DApp projects work together with a shared purpose. Complex Legacy Systems In many situations, tech companies begin exploring DApp development as part of a digital transformation project. However, this process can be expensive, especially when considering the investments made in legacy technology. Under these circumstances, Centrality’s catalogue of pre-built tools is particularly valuable, eliminating the cost of starting from scratch. Uncertainty In the WEF mentioned above, 59% of respondents said they had no confidence that the blockchain project would deliver a positive ROI before project commencement. As an emerging technology, this figure isn't particularly surprising, even in the face of limitations and scalability issues. The centrality platform helps allay fears of project success by dramatically improving scalability and lessening the risk of poor outcomes. survey BUIDLing in the Centrality Ecosystem Taking on incumbent market players with competing tech isn’t enough. As large organizations extend their advantage through , start-ups must leverage the values arising from collaborative processes. In this vein, the Centrality platform runs a consortium hybrid chain within the Ethereum network and uses several built-in improvements to performance and security. machine learning This infrastructure has been built to better-serve community members by: reducing latency and congestion on the platform; eliminating network fees when creating transactions; and circumventing stress on the public (Ethereum) network resulting from large transaction volumes. The Centrality ecosystem is made up of several underlying components, each interacting to provide a truly integrated development stack. This infrastructure underpins the primary building blocks of the Centrality platform. Source: The Centrality White Paper Sylo The majority of DApp projects migrating to the Centrality ecosystem choose to build within Sylo, a decentralized communication app and associated user interface. Comparable to WeChat, Sylo operates as "an app within an app." This super-app has a user base of over 80,000 and the functionality to support thousands of future "mini-apps." When users download Sylo, they gain immediate access to all Sylo-based apps within one platform. CENNZnet For those companies that wish to create a standalone application, the public CENNZnet blockchain supports the development of consumer-facing DApps. Under this model, projects deploy their smart contracts, but also gain access to the Centrality Developer Portal - a resource containing all the necessary app-building tools. If a resulting DApp proves particularly valuable to the broader ecosystem, the community can upvote it to a protocol with additional privileges, such as SingleSource and Sylo. PL^G Although CENNZnet underpins the majority of business in the Centrality ecosystem, some projects may require the flexibility of a fully customized blockchain application. The Centrality PL^G toolkit enables the seamless development of custom blockchain applications. Whether private or public, companies can create a blockchain with ease, avoiding the hassle of starting from scratch. For those projects requiring highly specific functionality, PL^G provides ultimate flexibility. Who’s Using Centrality? When Centrality’s native CENZZ tokens were issued via an ICO in early 2018, all tokens on offer - netting the company $80 million and placing it among the top ten biggest ICOs ever conducted. With a subsequent market capitalization falling in the (36th at the time of writing), ongoing performance speaks for itself. sold out within minutes top 50 Today, the Centrality platform is home to a diverse range of DApp projects, reinforcing initial expectations of robust adoption. Centrality apps have generated millions in revenue and have hundreds of thousands of users around the world - the Skoot app alone generates close to $100K a month. Based on the numbers, it's apparent that the ecosystem is gaining traction, validating its community-centric approach to DApp development. Source: Centrality Ecosystem DApps BUIDLing the Future of Blockchain As the competition for data, talent, and viable use cases heats up, the need for collaboration among start-ups will be essential to avoiding further industry consolidation. Facing this challenge head-on, Centrality offers a platform of services and a library of applications that act as DApp building blocks. Using this component-based model, startups are provided with ultimate flexibility over their projects while maintaining a cohesive feel across all ecosystem DApps. In an industry ripe with impressively polished user interfaces, this standardization facilitates the delivery of comparable user experiences across the Centrality ecosystem. There's no denying the future of blockchain is bright, and the same is true of those start-ups that choose to BUIDL with Centrality.

How One Blockchain Company BUIDLed Their Way to the Top 50

Case Study: Evaluating GNN Performance Using Shortest-Path Distance for Generalization and Fairness

The Systemic Impact of Deplatforming on Social Media: Results

Author:
(1) Melody Yu, Sage Hill School, California, USA. Table of Links Abstract and Introduction
Dataset
Methods
Results
Discussion
Conclusion, References, and Appendix III. METHODS We seek to establish a relationship between IMDb ratings and network indicators for a given TV series. Each temporal graph corresponds to ten scenes from an episode, whereas each IMDb review corresponds to one episode. Thus, to study the graph metrics for each episode, we first aggregate all segment graphs in one episode into one single weighted episode graph. Then, we study metrics on the aggregated episode graph. A. Segment Graph Aggregation B. Network Metrics We seek to establish a relationship between IMDb ratings and network indicators for a given TV series. Each temporal graph corresponds to ten scenes from an episode, whereas each IMDb review corresponds to one episode. Thus, to study the graph metrics for each episode, we first aggregate all segment graphs in one episode into one single weighted episode graph. Then, we study metrics on the aggregated episode graph. 1) Active Nodes: Active nodes are the total number of nodes in a graph with at least one edge connecting them to another node. The number of active nodes represents the number of characters that speak during an episode. We use the number of active nodes in an episode graph to describe the complexity of the episode plot in this study. 2) Network Density: A graph’s density is the ratio between the number of actual edges that exist in a graph to the maximum number of edges that the graph can have. It indicates how many of a graph’s potential connections are real connections. We define density as the following, where n represents the number of nodes and m represents the number of edges. Figure 4 shows the time series for the number of active nodes and the network density for different segment graphs from Game of Thrones episode 1. The density of the graph increases as more characters converse with one another. Characters in sparse graphs are often still connected, but the conversations are between two characters and not necessarily talk with many other characters. We use network density to measure the level of character interactions in each TV episode. 3) Node Strength: A node’s strength is defined as the sum of the strengths, or weights, of the node’s edges. Since the edge weight in our graph indicates the time spent conversing between two characters, the strength of a character is the total amount of time spent conversing with all other characters. Figure 5 shows the time series of characters with top 5 highest graph node strength from the Game of Thrones episode 1 graph. The maximum value of node strengths describes the total conversation time of the character who talked most with other characters during this episode. We use this metric to describe the exposure of the most active character in the episode. In this study, the TV episode graphs are usually separated into separate subgraphs, often without any connections between these subgraphs. To more accurately represent these subgraphs, we use local efficiency, calculated from the average value of a subgraph’s global efficiency. 5) Network Transitivity: Network transitivity is the overall probability for the network to have interconnected adjacent nodes, revealing the existence of tightly connected communities. It refers to the extent to which the relation that relates two nodes in a network that are connected by an edge is transitive. The network transitivity is the fraction of all possible triangles present in G. It is computed as dividing total number of node triangles in the graphs by the total number of “triads” (two edges with a shared vertex) Network transitivity is widely used to examine the level of clustering in social network analysis. In the content of character networks, high transitivity means that the network contains communities or groups of characters that are closely interacting with each other. We use network transitivity to measure the extent of the complex relationship between characters, and their relation to the plot. 6) Degree Centrality: In a connected graph, centrality is used to measure the importance of various nodes in a graph. In this study, we use various centrality metrics to measure the conversational interaction patterns of different characters in each TV episode. Degree centrality is the simplest measure of centrality. The degree of a node is defined as the number of edges or connections that a node has to other nodes. In the context of character networks in TV shows, the degree of a character represents the number of other characters that they interact with in a given episode. Figure 6 illustrates the time series of characters with the top 5 highest degrees in the character network from the first episode of Game of Thrones. A node with a higher degree than the rest of the nodes may be considered a central or pivotal character, as they have a large number of interactions with other characters. As our episode graph is aggregated from multiple segment graphs, the large number of interactions can be spread over different scenes, or concentrated in a few scenes involving many characters. In this research, we will use the distribution of node degrees, including the maximum value and standard deviation, to describe the patterns of conversational interactions among the characters in a given episode. 7) Closeness Centrality and Harmonic Centrality: The closeness centrality of a node in a connected graph, which is determined as the reciprocal of the sum of the shortest distances between the node and all other nodes in the graph, is a measure of centrality in a network. A node with a higher closeness centrality value is closer to all other nodes. However, in our study, the TV episode graph is not necessarily a connected graph, as there can be some characters forming subgroups that do not interact with characters outside their subgroup. Therefore, we use harmonic centrality instead of closeness centrality to allow disconnected networks in our research. The harmonic centrality of a node u is defined as the sum of reciprocals of the shortest path distances from all other nodes to node u. Two nodes that are unreachable to each other has a distance of infinity. In our study, we use harmonic centralization distribution (max value, standard deviation) to measure the extent to which the plot of the TV episode is concentrated on a single actor or a group of actors. 8) Eigenvector Centrality: While degree centrality measures the number of connections a node has, it does not take into account the influence of a node’s neighbors on its own importance. To address this, we can use eigenvector centrality, which considers the importance of a node’s neighbors in determining the node’s overall importance. A node with a high eigenvector score is connected to many other nodes with high scores, indicating that it is connected to influential characters. In the context of character networks in TV shows, a character with a high eigenvector centrality is likely to be connected to characters who are also considered influential within the network. This can be useful in understanding the importance of a character within the context of the overall plot and character relationships. This paper is available on arxiv under CC 4.0 license. Author: (1) Melody Yu, Sage Hill School, California, USA. Author: Author: (1) Melody Yu, Sage Hill School, California, USA. Table of Links Abstract and Introduction Dataset Methods Results Discussion Conclusion, References, and Appendix Abstract and Introduction Abstract and Introduction Dataset Dataset Methods Methods Results Results Discussion Discussion Conclusion, References, and Appendix Conclusion, References, and Appendix III. METHODS We seek to establish a relationship between IMDb ratings and network indicators for a given TV series. Each temporal graph corresponds to ten scenes from an episode, whereas each IMDb review corresponds to one episode. Thus, to study the graph metrics for each episode, we first aggregate all segment graphs in one episode into one single weighted episode graph. Then, we study metrics on the aggregated episode graph. A. Segment Graph Aggregation A. Segment Graph Aggregation B. Network Metrics B. Network Metrics We seek to establish a relationship between IMDb ratings and network indicators for a given TV series. Each temporal graph corresponds to ten scenes from an episode, whereas each IMDb review corresponds to one episode. Thus, to study the graph metrics for each episode, we first aggregate all segment graphs in one episode into one single weighted episode graph. Then, we study metrics on the aggregated episode graph. 1) Active Nodes: Active nodes are the total number of nodes in a graph with at least one edge connecting them to another node. The number of active nodes represents the number of characters that speak during an episode. We use the number of active nodes in an episode graph to describe the complexity of the episode plot in this study. 1) Active Nodes: 2) Network Density: A graph’s density is the ratio between the number of actual edges that exist in a graph to the maximum number of edges that the graph can have. It indicates how many of a graph’s potential connections are real connections. We define density as the following, where n represents the number of nodes and m represents the number of edges. 2) Network Density: Figure 4 shows the time series for the number of active nodes and the network density for different segment graphs from Game of Thrones episode 1. The density of the graph increases as more characters converse with one another. Characters in sparse graphs are often still connected, but the conversations are between two characters and not necessarily talk with many other characters. We use network density to measure the level of character interactions in each TV episode. 3) Node Strength: A node’s strength is defined as the sum of the strengths, or weights, of the node’s edges. Since the edge weight in our graph indicates the time spent conversing between two characters, the strength of a character is the total amount of time spent conversing with all other characters. 3) Node Strength: Figure 5 shows the time series of characters with top 5 highest graph node strength from the Game of Thrones episode 1 graph. The maximum value of node strengths describes the total conversation time of the character who talked most with other characters during this episode. We use this metric to describe the exposure of the most active character in the episode. In this study, the TV episode graphs are usually separated into separate subgraphs, often without any connections between these subgraphs. To more accurately represent these subgraphs, we use local efficiency, calculated from the average value of a subgraph’s global efficiency. 5) Network Transitivity: Network transitivity is the overall probability for the network to have interconnected adjacent nodes, revealing the existence of tightly connected communities. It refers to the extent to which the relation that relates two nodes in a network that are connected by an edge is transitive. The network transitivity is the fraction of all possible triangles present in G. It is computed as dividing total number of node triangles in the graphs by the total number of “triads” (two edges with a shared vertex) 5) Network Transitivity: Network transitivity is widely used to examine the level of clustering in social network analysis. In the content of character networks, high transitivity means that the network contains communities or groups of characters that are closely interacting with each other. We use network transitivity to measure the extent of the complex relationship between characters, and their relation to the plot. 6) Degree Centrality: In a connected graph, centrality is used to measure the importance of various nodes in a graph. In this study, we use various centrality metrics to measure the conversational interaction patterns of different characters in each TV episode. 6) Degree Centrality: Degree centrality is the simplest measure of centrality. The degree of a node is defined as the number of edges or connections that a node has to other nodes. In the context of character networks in TV shows, the degree of a character represents the number of other characters that they interact with in a given episode. Figure 6 illustrates the time series of characters with the top 5 highest degrees in the character network from the first episode of Game of Thrones. A node with a higher degree than the rest of the nodes may be considered a central or pivotal character, as they have a large number of interactions with other characters. As our episode graph is aggregated from multiple segment graphs, the large number of interactions can be spread over different scenes, or concentrated in a few scenes involving many characters. In this research, we will use the distribution of node degrees, including the maximum value and standard deviation, to describe the patterns of conversational interactions among the characters in a given episode. 7) Closeness Centrality and Harmonic Centrality: The closeness centrality of a node in a connected graph, which is determined as the reciprocal of the sum of the shortest distances between the node and all other nodes in the graph, is a measure of centrality in a network. A node with a higher closeness centrality value is closer to all other nodes. 7) Closeness Centrality and Harmonic Centrality: However, in our study, the TV episode graph is not necessarily a connected graph, as there can be some characters forming subgroups that do not interact with characters outside their subgroup. Therefore, we use harmonic centrality instead of closeness centrality to allow disconnected networks in our research. The harmonic centrality of a node u is defined as the sum of reciprocals of the shortest path distances from all other nodes to node u. Two nodes that are unreachable to each other has a distance of infinity. In our study, we use harmonic centralization distribution (max value, standard deviation) to measure the extent to which the plot of the TV episode is concentrated on a single actor or a group of actors. 8) Eigenvector Centrality: While degree centrality measures the number of connections a node has, it does not take into account the influence of a node’s neighbors on its own importance. To address this, we can use eigenvector centrality, which considers the importance of a node’s neighbors in determining the node’s overall importance. 8) Eigenvector Centrality: A node with a high eigenvector score is connected to many other nodes with high scores, indicating that it is connected to influential characters. In the context of character networks in TV shows, a character with a high eigenvector centrality is likely to be connected to characters who are also considered influential within the network. This can be useful in understanding the importance of a character within the context of the overall plot and character relationships. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Decoding the Popularity of TV Series: A Network Analysis Perspective: Methods

Author:
(1) Melody Yu, Sage Hill School, California, USA. Table of Links Abstract and Introduction
Dataset
Methods
Results
Discussion
Conclusion, References, and Appendix DISCUSSION The results of our research suggest that different character network structures may have different impacts on the quality of TV episodes. For example, the negative correlation between the number of active nodes and episode review scores in Game of Thrones may indicate that having too many characters in an episode can be overwhelming for viewers. On the other hand, for Breaking Bad, the positive correlation between the standard deviation of eigenvector centrality and episode review scores suggests that focusing on a smaller number of main characters is preferred over a wider focus on many different characters in a single episode. House of Cards had four correlations. The negative correlation between network efficiency and episode review implies that nodes are not in tight-knit groups - for example, character A might talk to character B who might talk to character C, but they might not talk in a group together. It also found a negative correlation between maximum harmonic centrality and episode review, implying again that characters have isolated conversations with one another rather than engaging in group discussions. Breaking Bad had a single positive correlation between network transitivity and episode review. This implies that episodes with tightly connected groups of characters tend to be preferred by viewers. It is important to note that a limitation of our study is that we only have the reviews for the episode as a whole, and not just for the character networks/dynamics. This means that many other factors such as cinematography, script writing, and plot, are all factored into a single review. Additionally, the placement and release of the episode are factors. For example, the finale of a series might be highly anticipated and receive a higher rating, or a guest star might appear in a particular episode and contribute to its score. Because the overall score of an episode represents all of these details together, we do not have the most accurate data to find a correlation between the character network and the metrics themselves. Our study is also limited to the types of metrics we tested. Though we tested many types, there are still many more network metrics that could potentially find greater correlations with the data. This paper is available on arxiv under CC 4.0 license. Author: (1) Melody Yu, Sage Hill School, California, USA. Author: Author: (1) Melody Yu, Sage Hill School, California, USA. Table of Links Abstract and Introduction Dataset Methods Results Discussion Conclusion, References, and Appendix Abstract and Introduction Abstract and Introduction Dataset Dataset Methods Methods Results Results Discussion Discussion Conclusion, References, and Appendix Conclusion, References, and Appendix DISCUSSION The results of our research suggest that different character network structures may have different impacts on the quality of TV episodes. For example, the negative correlation between the number of active nodes and episode review scores in Game of Thrones may indicate that having too many characters in an episode can be overwhelming for viewers. On the other hand, for Breaking Bad, the positive correlation between the standard deviation of eigenvector centrality and episode review scores suggests that focusing on a smaller number of main characters is preferred over a wider focus on many different characters in a single episode. House of Cards had four correlations. The negative correlation between network efficiency and episode review implies that nodes are not in tight-knit groups - for example, character A might talk to character B who might talk to character C, but they might not talk in a group together. It also found a negative correlation between maximum harmonic centrality and episode review, implying again that characters have isolated conversations with one another rather than engaging in group discussions. Breaking Bad had a single positive correlation between network transitivity and episode review. This implies that episodes with tightly connected groups of characters tend to be preferred by viewers. It is important to note that a limitation of our study is that we only have the reviews for the episode as a whole, and not just for the character networks/dynamics. This means that many other factors such as cinematography, script writing, and plot, are all factored into a single review. Additionally, the placement and release of the episode are factors. For example, the finale of a series might be highly anticipated and receive a higher rating, or a guest star might appear in a particular episode and contribute to its score. Because the overall score of an episode represents all of these details together, we do not have the most accurate data to find a correlation between the character network and the metrics themselves. Our study is also limited to the types of metrics we tested. Though we tested many types, there are still many more network metrics that could potentially find greater correlations with the data. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Decoding the Popularity of TV Series: A Network Analysis Perspective: Discussion

Author:
(1) Melody Yu, Sage Hill School, California, USA. Table of Links Abstract and Introduction
Dataset
Methods
Results
Discussion
Conclusion, References, and Appendix IV. RESULTS In this study, we calculated network metrics for three popular TV series: Game of Thrones, House of Cards, and Breaking Bad. A. Network Metrics We studied 22-26 episodes from the first three seasons of each show and calculated various network metrics for each episode. These metrics include density, efficiency, and transitivity, as well as node-level metrics such as degree, harmonic closeness centrality, and eigenvector closeness centrality. For each metric, we also calculated the maximum and standard deviation values for each episode’s character network. The results of these calculations are shown in Appendix Table V, VI, and VII , with each row representing a single episode and the first two columns indicating the episode number and review. The remaining columns display the various network metrics for that episode. B. Correlation between network metrics and episode reviews To determine whether there is a relationship between the network metrics we calculated and the IMDB reviews, we used correlation analysis. Specifically, we used the Spearman correlation method, as it is well-suited for analyzing ordinal data such as TV episode review scores. While the absolute value of a review score (e.g. 8.7) may not provide much insight on its own, it is generally accepted that an episode with a higher review score is of higher quality than one with a lower score. As such, the review scores can be considered ordinal, with higher values indicating better quality. By using the Spearman correlation method, we were able to examine the relationship between the network metrics and the episode reviews and determine if there is a correlation between them. Table I shows the results of the Spearman correlation analysis for the TV series Game of Thrones, examining the relationship between the network metrics and episode reviews. The analysis found two significant correlations: a negative correlation between the number of active nodes and episode review scores, and a positive correlation between the standard deviation of eigenvector centrality and episode review scores. Table II shows the results of the Spearman correlation analysis for the TV series House of Cards, examining the relationship between the network metrics and episode reviews. The table indicates that there are six significant correlations between the two variables. However, we note that two of these correlations may be influenced by influential outliers, as visualized in Figure 6. After accounting for these outliers, the analysis found four significant correlations: a negative correlation between network efficiency and episode review, a negative correlation between maximum node degree and episode review, a negative correlation between node degree standard deviation and episode review, and a negative correlation between maximum harmonic centrality and episode review. Table III shows the Spearman correlation between episode network metrics and episode reviews for the TV series Breaking Bad. One significant correlation between the network metrics and episode reviews is a positive correlation between network transitivity and episode review. This paper is available on arxiv under CC 4.0 license. Author: (1) Melody Yu, Sage Hill School, California, USA. Author: Author: (1) Melody Yu, Sage Hill School, California, USA. Table of Links Abstract and Introduction Dataset Methods Results Discussion Conclusion, References, and Appendix Abstract and Introduction Abstract and Introduction Dataset Dataset Methods Methods Results Results Discussion Discussion Conclusion, References, and Appendix Conclusion, References, and Appendix IV. RESULTS In this study, we calculated network metrics for three popular TV series: Game of Thrones, House of Cards, and Breaking Bad. A. Network Metrics A. Network Metrics We studied 22-26 episodes from the first three seasons of each show and calculated various network metrics for each episode. These metrics include density, efficiency, and transitivity, as well as node-level metrics such as degree, harmonic closeness centrality, and eigenvector closeness centrality. For each metric, we also calculated the maximum and standard deviation values for each episode’s character network. The results of these calculations are shown in Appendix Table V, VI, and VII , with each row representing a single episode and the first two columns indicating the episode number and review. The remaining columns display the various network metrics for that episode. B. Correlation between network metrics and episode reviews B. Correlation between network metrics and episode reviews To determine whether there is a relationship between the network metrics we calculated and the IMDB reviews, we used correlation analysis. Specifically, we used the Spearman correlation method, as it is well-suited for analyzing ordinal data such as TV episode review scores. While the absolute value of a review score (e.g. 8.7) may not provide much insight on its own, it is generally accepted that an episode with a higher review score is of higher quality than one with a lower score. As such, the review scores can be considered ordinal, with higher values indicating better quality. By using the Spearman correlation method, we were able to examine the relationship between the network metrics and the episode reviews and determine if there is a correlation between them. Table I shows the results of the Spearman correlation analysis for the TV series Game of Thrones, examining the relationship between the network metrics and episode reviews. The analysis found two significant correlations: a negative correlation between the number of active nodes and episode review scores, and a positive correlation between the standard deviation of eigenvector centrality and episode review scores. Table II shows the results of the Spearman correlation analysis for the TV series House of Cards, examining the relationship between the network metrics and episode reviews. The table indicates that there are six significant correlations between the two variables. However, we note that two of these correlations may be influenced by influential outliers, as visualized in Figure 6. After accounting for these outliers, the analysis found four significant correlations: a negative correlation between network efficiency and episode review, a negative correlation between maximum node degree and episode review, a negative correlation between node degree standard deviation and episode review, and a negative correlation between maximum harmonic centrality and episode review. Table III shows the Spearman correlation between episode network metrics and episode reviews for the TV series Breaking Bad. One significant correlation between the network metrics and episode reviews is a positive correlation between network transitivity and episode review. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Decoding the Popularity of TV Series: A Network Analysis Perspective: Results

A Novel Framework for Analyzing Economic News Narratives Using GPT-3.5: Results

Authors:
(1) Deborah Miori, Mathematical Institute, University of Oxford, Oxford, UK and 2Oxford-Man Institute of Quantitative Finance, Oxford, UK (Corresponding author: Deborah Miori, deborah.miori@maths.ox.ac.uk);
(2) Constantin Petrov, Fidelity Investments, London, UK. Table of Links Abstract and Intro Data Framework Results Conclusions, Acknowledgements, and References ABSTRACT Starting from a corpus of economic articles from The Wall Street Journal, we present a novel systematic way to analyse news content that evolves over time. We leverage on state-of-the-art natural language processing techniques (i.e. GPT3.5) to extract the most important entities of each article available, and aggregate co-occurrence of entities in a related graph at the weekly level. Network analysis techniques and fuzzy community detection are tested on the proposed set of graphs, and a framework is introduced that allows systematic but interpretable detection of topics and narratives. In parallel, we propose to consider the sentiment around main entities of an article as a more accurate proxy for the overall sentiment of such piece of text, and describe a case-study to motivate this choice. Finally, we design features that characterise the type and structure of news within each week, and map them to moments of financial markets dislocations. The latter are identified as dates with unusually high volatility across asset classes, and we find quantitative evidence that they relate to instances of high entropy in the high-dimensional space of interconnected news. This result further motivates the pursued efforts to provide a novel framework for the systematic analysis of narratives within news. 1 Introduction In today’s fast-paced digital age, the world is inundated with an unprecedented volume of information, particularly from news sources. The sheer magnitude of data generated daily has reached staggering proportions, making it increasingly challenging for individuals to parse and process this information accurately solely through human capabilities. News is constantly flowing from countless channels and platforms, and the need for advanced tools and technologies to sift through this vast sea of data has never been more apparent. In the realm of financial markets, the potential gain from efficiently handling such an enormous amount of news data, and extract quantitative signals from it, is even more pronounced. Financial markets are highly sensitive to information, and characterising narratives within news is surely one task that can enhance our knowledge on news’ impact on asset prices, trading strategies, and investor sentiment. Some examples of interesting research on the topic follow. In [1], the authors investigate the high-frequency interdependent relationships between the stock market and statistics on economic news in the US context. Then, [2] investigates how news affects the trading behaviour of different categories of investors in a financial market, while [3] finds evidence that market makers demand higher expected returns prior to earnings announcements, because of increased inventory risks that stem from holding net positions through the release of anticipated earnings news. In [4], the authors measure the correlation between the returns of publicly traded companies and news about them as collected from Yahoo Financial News. Then, [5] tries to quantify how topics discussed within news influence the stock market. The authors of this paper apply a topic modeling technique called Latent Dirichlet Allocation (LDA) [6], in order to extract the keywords of information (i.e. “topics”) that synchronise well with trading activity, measured by the daily trading volume. However, LDA is a primitive technique that cannot deliver a systematic and predictive advantage. Relatedly, the very recent research in [7] investigates the field of topic modeling in the context of finance-related news impact analysis, and further stresses how very limited literature exists. The authors compare three state-of-the-art topic models, namely LDA, Top2Vec [8], and BERTopic [9], and show that the latter performs best. The framework focuses on extracting topics and related sentiment of specific stocks, whose time series of returns are in connection investigated. However, the actual identification and interpretation of topics is still of questionable level, and the framework limits itself to facilitate efficient news selection based on topics, but do not properly analyse the latter. On the other hand, the recent emergence of novel Large Language Models (LLMs), such as OpenAI’s Generative Pre-trained Transformer (GPT) ones, clearly marks a significant departure from earlier language processing techniques. These models leverage the power of deep learning and vast amounts of text data to achieve unprecedented levels of language understanding and generation, achieving outstanding improvements in Natural Language Processing (NLP) tasks, and opening countless novel paths of research. The paper in [10] presents a study on harnessing LLMs outstanding knowledge and reasoning abilities for explainable financial time series forecasting of NASDAQ-100 stocks. Then, [11] leverages GPT parsing of companies’ annual reports, to get suggestions on investment targets. On the other hand, [12] shows the potential improvement of the GPT4 LLM in comparison to BERT for modeling same-day daily stock price movements of Apple and Tesla in 2017, based on sentiment analysis of microblogging messages. There is a clear increasing interest in understanding how to optimally leverage on the capabilities of GPT models, since the focus has otherwise lied on their qualitative responses. For now, quantitative applications have been very constrained in breadth, and we are also not aware of any existing research that tries to leverage GPTs for topic extraction and narrative detection within news. Thus, we believe that our research provides a strongly innovative approach to the solution of such tasks, by leveraging both GPT models and network analysis techniques. Main contributions. Our research introduces a novel framework for topic identification within economic news. Indeed, we leverage on GPT3.5 to extract the main entities (both concrete or abstract) of reference for each article in an available corpus, and aggregate entities’ co-occurrence among articles in weekly graphs. Studying the resultant set of graphs allows us to identify clusters of entities that can be mapped back to interpretable non-trivial topics. Furthermore, basic metric such as degree and eigenvector centrality of nodes are shown to characterise the evolution over time of central themes of discussion. Beyond that, we propose to consider the sentiment around main entities of an article as a more accurate proxy for the overall sentiment of such piece of text, and describe a case-study to motivate this choice. One final contribution of our study is the investigation of news’ features in relation to financial market dislocations. We design attributes that characterise both the local and global structure of news, and their sentiment and interconnections. This allows us to find quantitative evidence of high entropy in the high-dimensional space of interconnected news, when the latter are associated to moments of unusually high volatility across asset classes. Structure of the paper. Section 2 introduces the data we collect, and some initial related processing. Section 3 clarifies the theoretical knowledge that is necessary to understand the approach taken, which is mainly comprised by notions from both NLP and graph theory, and on related embedding techniques. Then, Section 4 highlights our analyses on narratives and market dislocations, and describes the results achieved. Finally, we conclude this work with some last remarks in Section 5. This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Deborah Miori, Mathematical Institute, University of Oxford, Oxford, UK and 2Oxford-Man Institute of Quantitative Finance, Oxford, UK (Corresponding author: Deborah Miori, deborah.miori@maths.ox.ac.uk); (2) Constantin Petrov, Fidelity Investments, London, UK. Authors: Authors: (1) Deborah Miori, Mathematical Institute, University of Oxford, Oxford, UK and 2Oxford-Man Institute of Quantitative Finance, Oxford, UK (Corresponding author: Deborah Miori, deborah.miori@maths.ox.ac.uk); (2) Constantin Petrov, Fidelity Investments, London, UK. Table of Links Abstract and Intro Abstract and Intro Data Data Framework Framework Results Results Conclusions, Acknowledgements, and References Conclusions, Acknowledgements, and References ABSTRACT Starting from a corpus of economic articles from The Wall Street Journal, we present a novel systematic way to analyse news content that evolves over time. We leverage on state-of-the-art natural language processing techniques (i.e. GPT3.5) to extract the most important entities of each article available, and aggregate co-occurrence of entities in a related graph at the weekly level. Network analysis techniques and fuzzy community detection are tested on the proposed set of graphs, and a framework is introduced that allows systematic but interpretable detection of topics and narratives. In parallel, we propose to consider the sentiment around main entities of an article as a more accurate proxy for the overall sentiment of such piece of text, and describe a case-study to motivate this choice. Finally, we design features that characterise the type and structure of news within each week, and map them to moments of financial markets dislocations. The latter are identified as dates with unusually high volatility across asset classes, and we find quantitative evidence that they relate to instances of high entropy in the high-dimensional space of interconnected news. This result further motivates the pursued efforts to provide a novel framework for the systematic analysis of narratives within news. 1 Introduction In today’s fast-paced digital age, the world is inundated with an unprecedented volume of information, particularly from news sources. The sheer magnitude of data generated daily has reached staggering proportions, making it increasingly challenging for individuals to parse and process this information accurately solely through human capabilities. News is constantly flowing from countless channels and platforms, and the need for advanced tools and technologies to sift through this vast sea of data has never been more apparent. In the realm of financial markets, the potential gain from efficiently handling such an enormous amount of news data, and extract quantitative signals from it, is even more pronounced. Financial markets are highly sensitive to information, and characterising narratives within news is surely one task that can enhance our knowledge on news’ impact on asset prices, trading strategies, and investor sentiment. Some examples of interesting research on the topic follow. In [1], the authors investigate the high-frequency interdependent relationships between the stock market and statistics on economic news in the US context. Then, [2] investigates how news affects the trading behaviour of different categories of investors in a financial market, while [3] finds evidence that market makers demand higher expected returns prior to earnings announcements, because of increased inventory risks that stem from holding net positions through the release of anticipated earnings news. In [4], the authors measure the correlation between the returns of publicly traded companies and news about them as collected from Yahoo Financial News. Then, [5] tries to quantify how topics discussed within news influence the stock market. The authors of this paper apply a topic modeling technique called Latent Dirichlet Allocation (LDA) [6], in order to extract the keywords of information (i.e. “topics”) that synchronise well with trading activity, measured by the daily trading volume. However, LDA is a primitive technique that cannot deliver a systematic and predictive advantage. Relatedly, the very recent research in [7] investigates the field of topic modeling in the context of finance-related news impact analysis, and further stresses how very limited literature exists. The authors compare three state-of-the-art topic models, namely LDA, Top2Vec [8], and BERTopic [9], and show that the latter performs best. The framework focuses on extracting topics and related sentiment of specific stocks, whose time series of returns are in connection investigated. However, the actual identification and interpretation of topics is still of questionable level, and the framework limits itself to facilitate efficient news selection based on topics, but do not properly analyse the latter. On the other hand, the recent emergence of novel Large Language Models (LLMs), such as OpenAI’s Generative Pre-trained Transformer (GPT) ones, clearly marks a significant departure from earlier language processing techniques. These models leverage the power of deep learning and vast amounts of text data to achieve unprecedented levels of language understanding and generation, achieving outstanding improvements in Natural Language Processing (NLP) tasks, and opening countless novel paths of research. The paper in [10] presents a study on harnessing LLMs outstanding knowledge and reasoning abilities for explainable financial time series forecasting of NASDAQ-100 stocks. Then, [11] leverages GPT parsing of companies’ annual reports, to get suggestions on investment targets. On the other hand, [12] shows the potential improvement of the GPT4 LLM in comparison to BERT for modeling same-day daily stock price movements of Apple and Tesla in 2017, based on sentiment analysis of microblogging messages. There is a clear increasing interest in understanding how to optimally leverage on the capabilities of GPT models, since the focus has otherwise lied on their qualitative responses. For now, quantitative applications have been very constrained in breadth, and we are also not aware of any existing research that tries to leverage GPTs for topic extraction and narrative detection within news. Thus, we believe that our research provides a strongly innovative approach to the solution of such tasks, by leveraging both GPT models and network analysis techniques. Main contributions. Our research introduces a novel framework for topic identification within economic news. Indeed, we leverage on GPT3.5 to extract the main entities (both concrete or abstract) of reference for each article in an available corpus, and aggregate entities’ co-occurrence among articles in weekly graphs. Studying the resultant set of graphs allows us to identify clusters of entities that can be mapped back to interpretable non-trivial topics. Furthermore, basic metric such as degree and eigenvector centrality of nodes are shown to characterise the evolution over time of central themes of discussion. Beyond that, we propose to consider the sentiment around main entities of an article as a more accurate proxy for the overall sentiment of such piece of text, and describe a case-study to motivate this choice. One final contribution of our study is the investigation of news’ features in relation to financial market dislocations. We design attributes that characterise both the local and global structure of news, and their sentiment and interconnections. This allows us to find quantitative evidence of high entropy in the high-dimensional space of interconnected news, when the latter are associated to moments of unusually high volatility across asset classes. Main contributions. Structure of the paper. Section 2 introduces the data we collect, and some initial related processing. Section 3 clarifies the theoretical knowledge that is necessary to understand the approach taken, which is mainly comprised by notions from both NLP and graph theory, and on related embedding techniques. Then, Section 4 highlights our analyses on narratives and market dislocations, and describes the results achieved. Finally, we conclude this work with some last remarks in Section 5. Structure of the paper. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

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