As public entities like brands and politicians increasingly rely on social media to engage their constituents, analyzing who follows them can reveal information about how they are perceived. Whereas most prior work considers following networks as unweighted directed graphs, in this paper we use a tie strength model to place weights on follow links to estimate the strength of relationship between users. We use conversational signals (retweets, mentions) as a proxy class label for a binary classification problem, using social and linguistic features to estimate tie strength. We then apply this approach to a case study estimating how brands are perceived with respect to certain issues (e.g., how environmentally friendly is Patagonia perceived to be?). We compute weighted follower overlap scores to measure the similarity between brands and exemplar accounts (e.g., environmental non-profits), finding that the tie strength scores can provide more nuanced estimates of consumer perception.
{"title":"Estimating Tie Strength in Follower Networks to Measure Brand Perceptions","authors":"T. Nguyen, Li Zhang, A. Culotta","doi":"10.1145/3341161.3343675","DOIUrl":"https://doi.org/10.1145/3341161.3343675","url":null,"abstract":"As public entities like brands and politicians increasingly rely on social media to engage their constituents, analyzing who follows them can reveal information about how they are perceived. Whereas most prior work considers following networks as unweighted directed graphs, in this paper we use a tie strength model to place weights on follow links to estimate the strength of relationship between users. We use conversational signals (retweets, mentions) as a proxy class label for a binary classification problem, using social and linguistic features to estimate tie strength. We then apply this approach to a case study estimating how brands are perceived with respect to certain issues (e.g., how environmentally friendly is Patagonia perceived to be?). We compute weighted follower overlap scores to measure the similarity between brands and exemplar accounts (e.g., environmental non-profits), finding that the tie strength scores can provide more nuanced estimates of consumer perception.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122461101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As the world is flooded with deluge of data, the demand for mining data to gain insights is increasing. One effective technique to deal with the problem is to model the data as networks (graphs) and then apply graph mining techniques to uncover useful patterns. Several graph mining techniques have been studied in the literature, and graphlet-based analysis is gaining popularity due to its power in exposing hidden structure and interaction within the networks. The concept of graphlets for basic (undirected) networks was introduced around 2004 by Pržulj, et. al. [14]. Subsequently, graphlet based network analysis gained attraction when Pržulj added the concept of graphlet orbits and applied to biological networks [15]. A decade later, Sarajlić, et. al. introduced graphlets and graphlet orbits for directed networks, illustrating its application to fields beyond biology such as world trade networks, brain networks, communication networks, etc. [19]. Hence, directed graphlets are found to be more powerful in exposing hidden structures of the network than undirected graphlets of same size, due to added information on the edges. Taking this approach further, more recently, graphlets and orbits for signed networks have been introduced by Dale [3]. This paper presents a simple algorithm to enumerate signed graphlets and orbits. It then demonstrates an application of signed graphlets and orbits to a metabolic network.
{"title":"Algorithm and Application for Signed Graphlets","authors":"Apratim Das, A. Aravind, Mark Dale","doi":"10.1145/3341161.3343692","DOIUrl":"https://doi.org/10.1145/3341161.3343692","url":null,"abstract":"As the world is flooded with deluge of data, the demand for mining data to gain insights is increasing. One effective technique to deal with the problem is to model the data as networks (graphs) and then apply graph mining techniques to uncover useful patterns. Several graph mining techniques have been studied in the literature, and graphlet-based analysis is gaining popularity due to its power in exposing hidden structure and interaction within the networks. The concept of graphlets for basic (undirected) networks was introduced around 2004 by Pržulj, et. al. [14]. Subsequently, graphlet based network analysis gained attraction when Pržulj added the concept of graphlet orbits and applied to biological networks [15]. A decade later, Sarajlić, et. al. introduced graphlets and graphlet orbits for directed networks, illustrating its application to fields beyond biology such as world trade networks, brain networks, communication networks, etc. [19]. Hence, directed graphlets are found to be more powerful in exposing hidden structures of the network than undirected graphlets of same size, due to added information on the edges. Taking this approach further, more recently, graphlets and orbits for signed networks have been introduced by Dale [3]. This paper presents a simple algorithm to enumerate signed graphlets and orbits. It then demonstrates an application of signed graphlets and orbits to a metabolic network.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121919143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amir Pouran Ben Veyseh, M. Thai, Thien Huu Nguyen, D. Dou
Fake news and rumors constitute a major problem in social networks recently. Due to the fast information propagation in social networks, it is inefficient to use human labor to detect suspicious news. Automatic rumor detection is thus necessary to prevent devastating effects of rumors on the individuals and society. Previous work has shown that in addition to the content of the news/posts and their contexts (i.e., replies), the relations or connections among those components are important to boost the rumor detection performance. In order to induce such relations between posts and contexts, the prior work has mainly relied on the inherent structures of the social networks (e.g., direct replies), ignoring the potential semantic connections between those objects. In this work, we demonstrate that such semantic relations are also helpful as they can reveal the implicit structures to better capture the patterns in the contexts for rumor detection. We propose to employ the self-attention mechanism in neural text modeling to achieve the semantic structure induction for this problem. In addition, we introduce a novel method to preserve the important information of the main news/posts in the final representations of the entire threads to further improve the performance for rumor detection. Our method matches the main post representations and the thread representations by ensuring that they predict the same latent labels in a multitask learning framework. The extensive experiments demonstrate the effectiveness of the proposed model for rumor detection, yielding the state-of-the-art performance on recent datasets for this problem.
{"title":"Rumor Detection in Social Networks via Deep Contextual Modeling","authors":"Amir Pouran Ben Veyseh, M. Thai, Thien Huu Nguyen, D. Dou","doi":"10.1145/3341161.3342896","DOIUrl":"https://doi.org/10.1145/3341161.3342896","url":null,"abstract":"Fake news and rumors constitute a major problem in social networks recently. Due to the fast information propagation in social networks, it is inefficient to use human labor to detect suspicious news. Automatic rumor detection is thus necessary to prevent devastating effects of rumors on the individuals and society. Previous work has shown that in addition to the content of the news/posts and their contexts (i.e., replies), the relations or connections among those components are important to boost the rumor detection performance. In order to induce such relations between posts and contexts, the prior work has mainly relied on the inherent structures of the social networks (e.g., direct replies), ignoring the potential semantic connections between those objects. In this work, we demonstrate that such semantic relations are also helpful as they can reveal the implicit structures to better capture the patterns in the contexts for rumor detection. We propose to employ the self-attention mechanism in neural text modeling to achieve the semantic structure induction for this problem. In addition, we introduce a novel method to preserve the important information of the main news/posts in the final representations of the entire threads to further improve the performance for rumor detection. Our method matches the main post representations and the thread representations by ensuring that they predict the same latent labels in a multitask learning framework. The extensive experiments demonstrate the effectiveness of the proposed model for rumor detection, yielding the state-of-the-art performance on recent datasets for this problem.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130186993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The United Nations, in their annual World Drug Report in 2018, reported that the production of Opium, Cocaine, Cannabis, etc. all observed record highs, which indicates the ever-growing demand of these drugs. Social networks of individuals associated with Drug Trafficking Organizations (DTO) have been created and studied by various research groups to capture key individuals, in order to disrupt operations of a DTO. With drug offenses increasing globally, the list of suspect individuals has also been growing over the past decade. As it takes significant amount of technical and human resources to monitor a suspect, an increasing list entails higher resource requirements on the part of law enforcement agencies. Monitoring all the suspects soon becomes an impossible task. In this paper, we present a novel methodology which ensures reduction in resources on the part of law enforcement authorities, without compromising the ability to uniquely identify a suspect, when they become “active” in drug related activities. Our approach utilizes the mathematical notion of Identifying Codes, which generates unique identification for all the nodes in a network. We find that just monitoring important individuals in the network leads to a wastage in resources and show how our approach overcomes this shortcoming. Finally, we evaluate the efficacy of our approach on real world datasets.
{"title":"Monitoring Individuals in Drug Trafficking Organizations: A Social Network Analysis","authors":"K. Basu, Arunabha Sen","doi":"10.1145/3341161.3342938","DOIUrl":"https://doi.org/10.1145/3341161.3342938","url":null,"abstract":"The United Nations, in their annual World Drug Report in 2018, reported that the production of Opium, Cocaine, Cannabis, etc. all observed record highs, which indicates the ever-growing demand of these drugs. Social networks of individuals associated with Drug Trafficking Organizations (DTO) have been created and studied by various research groups to capture key individuals, in order to disrupt operations of a DTO. With drug offenses increasing globally, the list of suspect individuals has also been growing over the past decade. As it takes significant amount of technical and human resources to monitor a suspect, an increasing list entails higher resource requirements on the part of law enforcement agencies. Monitoring all the suspects soon becomes an impossible task. In this paper, we present a novel methodology which ensures reduction in resources on the part of law enforcement authorities, without compromising the ability to uniquely identify a suspect, when they become “active” in drug related activities. Our approach utilizes the mathematical notion of Identifying Codes, which generates unique identification for all the nodes in a network. We find that just monitoring important individuals in the network leads to a wastage in resources and show how our approach overcomes this shortcoming. Finally, we evaluate the efficacy of our approach on real world datasets.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"272 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133852291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sheung Yat Law, D. Kasthurirathna, Piraveenan Mahendra
Social systems are increasingly being modelled as complex networks, and the interactions and decision making of individuals in such systems can be modelled using game theory. Therefore, networked game theory can be effectively used to model social dynamics. Individuals can use pure or mixed strategies in their decision making, and recent research has shown that there is a connection between the topological placement of an individual within a social network and the best strategy they can choose to maximise their returns. Therefore, if certain individuals have a preference to employ a certain strategy, they can be swapped or moved around within the social network to more desirable topological locations where their chosen strategies will be more effective. To this end, it has been shown that to increase the overall public good, the cooperators should be placed at the hubs, and the defectors should be placed at the peripheral nodes. In this paper, we tackle a related question, which is the time (or number of swaps) it takes for individuals who are randomly placed within the network to move to optimal topological locations which ensure that the public utility satisfies a certain utility threshold. We show that this time depends on the topology of the social network, and we analyse this topological dependence in terms of topological metrics such as scale-free exponent, assortativity, clustering coefficient, and Shannon information content. We show that the higher the scale-free exponent, the quicker the public utility threshold can be reached by swapping individuals from an initial random allocation. On the other hand, we find that assortativity has negative correlation with the time it takes to reach the public utility threshold. We find also that in terms of the correlation between information content and the time it takes to reach a public utility threshold from a random initial assignment, there is a bifurcation: one class of networks show a positive correlation, while another shows a negative correlation. Our results highlight that by designing networks with appropriate topological properties, one can minimise the need for the movement of individuals within a network before a certain public good threshold is achieved. This result has obvious implications for defence strategies in particular.
{"title":"Placement matters in making good decisions sooner: the influence of topology in reaching public utility thresholds","authors":"Sheung Yat Law, D. Kasthurirathna, Piraveenan Mahendra","doi":"10.1145/3341161.3343674","DOIUrl":"https://doi.org/10.1145/3341161.3343674","url":null,"abstract":"Social systems are increasingly being modelled as complex networks, and the interactions and decision making of individuals in such systems can be modelled using game theory. Therefore, networked game theory can be effectively used to model social dynamics. Individuals can use pure or mixed strategies in their decision making, and recent research has shown that there is a connection between the topological placement of an individual within a social network and the best strategy they can choose to maximise their returns. Therefore, if certain individuals have a preference to employ a certain strategy, they can be swapped or moved around within the social network to more desirable topological locations where their chosen strategies will be more effective. To this end, it has been shown that to increase the overall public good, the cooperators should be placed at the hubs, and the defectors should be placed at the peripheral nodes. In this paper, we tackle a related question, which is the time (or number of swaps) it takes for individuals who are randomly placed within the network to move to optimal topological locations which ensure that the public utility satisfies a certain utility threshold. We show that this time depends on the topology of the social network, and we analyse this topological dependence in terms of topological metrics such as scale-free exponent, assortativity, clustering coefficient, and Shannon information content. We show that the higher the scale-free exponent, the quicker the public utility threshold can be reached by swapping individuals from an initial random allocation. On the other hand, we find that assortativity has negative correlation with the time it takes to reach the public utility threshold. We find also that in terms of the correlation between information content and the time it takes to reach a public utility threshold from a random initial assignment, there is a bifurcation: one class of networks show a positive correlation, while another shows a negative correlation. Our results highlight that by designing networks with appropriate topological properties, one can minimise the need for the movement of individuals within a network before a certain public good threshold is achieved. This result has obvious implications for defence strategies in particular.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"1993 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131145538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper examines quantity and quality superposter value creation within Coursera Massive Open Online Courses (MOOC) forums using a social network analysis (SNA) approach. The value of quantity superposters (i.e. students who post significantly more often than the majority of students) and quality superposters (i.e. students who receive significantly more upvotes than the majority of students) is assessed using Stochastic Actor-Oriented Modeling (SAOM) and network centrality calculations. Overall, quantity and quality superposting was found to have a significant effect on tie formation within the discussion networks. In addition, quantity and quality superposters were found to have higher-than-average information brokerage capital within their networks.
{"title":"Examining MOOC superposter behavior using social network analysis","authors":"M. Hegde, I. McCulloh, J. Piorkowski","doi":"10.1145/3341161.3345310","DOIUrl":"https://doi.org/10.1145/3341161.3345310","url":null,"abstract":"This paper examines quantity and quality superposter value creation within Coursera Massive Open Online Courses (MOOC) forums using a social network analysis (SNA) approach. The value of quantity superposters (i.e. students who post significantly more often than the majority of students) and quality superposters (i.e. students who receive significantly more upvotes than the majority of students) is assessed using Stochastic Actor-Oriented Modeling (SAOM) and network centrality calculations. Overall, quantity and quality superposting was found to have a significant effect on tie formation within the discussion networks. In addition, quantity and quality superposters were found to have higher-than-average information brokerage capital within their networks.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128438598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dionisis Margaris, D. Spiliotopoulos, C. Vassilakis
Online advertising benefits by recommender systems since the latter analyse reviews and rating of products, providing useful insight of the buyer perception of products and services. When traditional recommender system information is enriched with social network information, more successful recommendations are produced, since more users' aspects are taken into consideration. However, social network information may be unavailable since some users may not have social network accounts or may not consent to their use for recommendations, while rating data may be unavailable due to the cold start phenomenon. In this paper, we propose an algorithm that combines limited collaborative filtering information, comprised only of users' ratings on items, with limited social network information, comprised only of users' social relations, in order to improve (1) prediction accuracy and (2) prediction coverage in collaborative filtering recommender systems, at the same time. The proposed algorithm considerably improves rating prediction accuracy and coverage, while it can be easily integrated in recommender systems.
{"title":"Social Relations versus Near Neighbours: Reliable Recommenders in Limited Information Social Network Collaborative Filtering for Online Advertising","authors":"Dionisis Margaris, D. Spiliotopoulos, C. Vassilakis","doi":"10.1145/3341161.3345620","DOIUrl":"https://doi.org/10.1145/3341161.3345620","url":null,"abstract":"Online advertising benefits by recommender systems since the latter analyse reviews and rating of products, providing useful insight of the buyer perception of products and services. When traditional recommender system information is enriched with social network information, more successful recommendations are produced, since more users' aspects are taken into consideration. However, social network information may be unavailable since some users may not have social network accounts or may not consent to their use for recommendations, while rating data may be unavailable due to the cold start phenomenon. In this paper, we propose an algorithm that combines limited collaborative filtering information, comprised only of users' ratings on items, with limited social network information, comprised only of users' social relations, in order to improve (1) prediction accuracy and (2) prediction coverage in collaborative filtering recommender systems, at the same time. The proposed algorithm considerably improves rating prediction accuracy and coverage, while it can be easily integrated in recommender systems.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130279755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Detecting diseases at early stage can help to overcome and treat them accurately. Identifying the appropriate treatment depends on the method that is used in diagnosing the diseases. A Clinical Decision Support System (CDS) can greatly help in identifying diseases and methods of treatment. In this paper we propose a CDS framework that can integrate heterogeneous health data from different sources, such as laboratory test results, basic information of patients, and health records. Using the electronic health medical data so collected, innovative machine learning and deep learning approaches are employed to implement a set of services to recommend a list of diseases and thus assist physicians in diagnosing or treating their patients health issues more efficiently.
{"title":"A clinical decision support framework for automatic disease diagnoses","authors":"C. Comito, Agostino Forestiero, Giuseppe Papuzzo","doi":"10.1145/3341161.3343509","DOIUrl":"https://doi.org/10.1145/3341161.3343509","url":null,"abstract":"Detecting diseases at early stage can help to overcome and treat them accurately. Identifying the appropriate treatment depends on the method that is used in diagnosing the diseases. A Clinical Decision Support System (CDS) can greatly help in identifying diseases and methods of treatment. In this paper we propose a CDS framework that can integrate heterogeneous health data from different sources, such as laboratory test results, basic information of patients, and health records. Using the electronic health medical data so collected, innovative machine learning and deep learning approaches are employed to implement a set of services to recommend a list of diseases and thus assist physicians in diagnosing or treating their patients health issues more efficiently.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"69 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131470999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In recent years, substantial effort has been devoted to learning to represent the static graphs and their substructures. A few studies explored utilizing temporal information available in a dynamic setting in order to address the node representation learning. However, the representation learning problem for the entire graph in a dynamic context is yet to be addressed. In this paper, we propose an unsupervised encoder-decoder framework that projects a dynamic graph at each time step into a d-dimensional space, taking into account both the graph's topology and dynamics. We investigate two different strategies. First, we address the representation learning problem by auto-encoding the graph dynamics. Second, we formulate a graph prediction problem and enforce the encoder to learn the representation that an autoregressive decoder then uses to predict the future of a dynamic graph. Gated graph neural networks (GGNNs) are incorporated to learn the topology of the graph at each time step and Long short-term memory networks (LSTMs) are leveraged to propagate the temporal information among the nodes through time. We demonstrate the efficacy of our approach with a graph classification task using two real-world datasets of animal behaviour and brain networks.
{"title":"Predictive Temporal Embedding of Dynamic Graphs","authors":"Aynaz Taheri, T. Berger-Wolf","doi":"10.1145/3341161.3342872","DOIUrl":"https://doi.org/10.1145/3341161.3342872","url":null,"abstract":"In recent years, substantial effort has been devoted to learning to represent the static graphs and their substructures. A few studies explored utilizing temporal information available in a dynamic setting in order to address the node representation learning. However, the representation learning problem for the entire graph in a dynamic context is yet to be addressed. In this paper, we propose an unsupervised encoder-decoder framework that projects a dynamic graph at each time step into a d-dimensional space, taking into account both the graph's topology and dynamics. We investigate two different strategies. First, we address the representation learning problem by auto-encoding the graph dynamics. Second, we formulate a graph prediction problem and enforce the encoder to learn the representation that an autoregressive decoder then uses to predict the future of a dynamic graph. Gated graph neural networks (GGNNs) are incorporated to learn the topology of the graph at each time step and Long short-term memory networks (LSTMs) are leveraged to propagate the temporal information among the nodes through time. We demonstrate the efficacy of our approach with a graph classification task using two real-world datasets of animal behaviour and brain networks.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"82 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128152140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
There is growing concern about the use of social platforms to push political narratives during elections. One very recent case is Brazil's, where WhatsApp is now widely perceived as a key enabler of the far-right's rise to power. In this paper, we perform a large-scale analysis of partisan WhatsApp groups to shed light on how both right-wingers and left-wingers used the platform in the 2018 Brazilian presidential election. Across its two rounds, we collected +2.8M messages from +45k users in 232 public groups (175 right-wing vs. 57 left-wing). After describing how we obtained a sample that is many times larger than previous works, we contrast right-wingers and left-wingers on their social network metrics, regional distribution of users, content-sharing habits, and most characteristic news sources.
{"title":"Thousands of Small, Constant Rallies: A Large-Scale Analysis of Partisan WhatsApp Groups","authors":"Victor S. Bursztyn, L. Birnbaum","doi":"10.1145/3341161.3342905","DOIUrl":"https://doi.org/10.1145/3341161.3342905","url":null,"abstract":"There is growing concern about the use of social platforms to push political narratives during elections. One very recent case is Brazil's, where WhatsApp is now widely perceived as a key enabler of the far-right's rise to power. In this paper, we perform a large-scale analysis of partisan WhatsApp groups to shed light on how both right-wingers and left-wingers used the platform in the 2018 Brazilian presidential election. Across its two rounds, we collected +2.8M messages from +45k users in 232 public groups (175 right-wing vs. 57 left-wing). After describing how we obtained a sample that is many times larger than previous works, we contrast right-wingers and left-wingers on their social network metrics, regional distribution of users, content-sharing habits, and most characteristic news sources.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129079433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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