Pub Date : 2023-06-02DOI: 10.1609/icwsm.v17i1.22166
Sian Lee, Haeseung Seo, Dongwon Lee, Aiping Xiong
Associative inference is an adaptive, constructive process of memory that allows people to link related information to make novel connections. We conducted three online human-subjects experiments investigating participants’ susceptibility to associatively inferred misinformation and its interaction with their cognitive ability and how news articles were presented. In each experiment, participants completed recognition and perceived accuracy rating tasks for the snippets of news articles in a tweet format across two phases. At Phase 1, participants viewed real news only. At Phase 2, participants viewed both real and fake news. Critically, we varied whether the fake news at Phase 2 was inferred from (i.e., associative inference), associated with (i.e., association only), or irrelevant to (i.e., control) the corresponding real news pairs at Phase 1. Both recognition and perceived accuracy results showed that participants in the associative inference condition were more susceptible to fake news than those in the other conditions. Furthermore, hashtags embedded within the tweets made the obtained effects evident only for the participants of higher cognitive ability. Our findings reveal that associative inference can be a basis for individuals’ susceptibility to misinformation, especially for those of higher cognitive ability. We conclude by discussing the implications of our results for understanding and mitigating misinformation on social media platforms.
{"title":"Associative Inference Can Increase People's Susceptibility to Misinformation","authors":"Sian Lee, Haeseung Seo, Dongwon Lee, Aiping Xiong","doi":"10.1609/icwsm.v17i1.22166","DOIUrl":"https://doi.org/10.1609/icwsm.v17i1.22166","url":null,"abstract":"Associative inference is an adaptive, constructive process of memory that allows people to link related information to make novel connections. We conducted three online human-subjects experiments investigating participants’ susceptibility to associatively inferred misinformation and its interaction with their cognitive ability and how news articles were presented. In each experiment, participants completed recognition and perceived accuracy rating tasks for the snippets of news articles in a tweet format across two phases. At Phase 1, participants viewed real news only. At Phase 2, participants viewed both real and fake news. Critically, we varied whether the fake news at Phase 2 was inferred from (i.e., associative inference), associated with (i.e., association only), or irrelevant to (i.e., control) the corresponding real news pairs at Phase 1. Both recognition and perceived accuracy results showed that participants in the associative inference condition were more susceptible to fake news than those in the other conditions. Furthermore, hashtags embedded within the tweets made the obtained effects evident only for the participants of higher cognitive ability. Our findings reveal that associative inference can be a basis for individuals’ susceptibility to misinformation, especially for those of higher cognitive ability. We conclude by discussing the implications of our results for understanding and mitigating misinformation on social media platforms.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129512910","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}
Pub Date : 2023-06-02DOI: 10.1609/icwsm.v17i1.22129
C. Chelmis, Bedirhan Gergin
People increasingly use the Internet to make food-related choices, prompting research on food recommendation systems. Recently, works that incorporate nutritional constraints into the recommendation process have been proposed to promote healthier recipes. Ingredient substitution is also used, particularly by people motivated to reduce the intake of a specific nutrient or in order to avoid a particular category of ingredients due for instance to allergies. This study takes a complementary approach towards empowering people to make healthier food choices by simplifying the process of identifying plausible recipe substitutions. To achieve this goal, this work constructs a large-scale network of similar recipes, and analyzes this network to reveal interesting properties that have important implications to the development of food recommendation systems.
{"title":"Recipe Networks and the Principles of Healthy Food on the Web","authors":"C. Chelmis, Bedirhan Gergin","doi":"10.1609/icwsm.v17i1.22129","DOIUrl":"https://doi.org/10.1609/icwsm.v17i1.22129","url":null,"abstract":"People increasingly use the Internet to make food-related choices, prompting research on food recommendation systems. Recently, works that incorporate nutritional constraints into the recommendation process have been proposed to promote healthier recipes. Ingredient substitution is also used, particularly by people motivated to reduce the intake of a specific nutrient or in order to avoid a particular category of ingredients due for instance to allergies. This study takes a complementary approach towards empowering people to make healthier food choices by simplifying the process of identifying plausible recipe substitutions. To achieve this goal, this work constructs a large-scale network of similar recipes, and analyzes this network to reveal interesting properties that have important implications to the development of food recommendation systems.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116740591","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}
Pub Date : 2023-06-02DOI: 10.1609/icwsm.v17i1.22148
Khuzaima Hameed, Rob Johnston, Brent Younce, Minh Tang, Alyson Wilson
State-backed platform manipulation (SBPM) on Twitter has been a prominent public issue since the 2016 US election cycle. Identifying and characterizing users on Twitter as belonging to a state-backed campaign is an important part of mitigating their influence. In this paper, we propose a novel time series feature grounded in social science to characterize dynamic user networks on Twitter. We introduce a classification approach, motif functional data analysis (MFDA), that captures the evolution of motifs in temporal networks, which is a useful feature for analyzing malign influence. We evaluate MFDA on data from known SBPM campaigns on Twitter and representative authentic data and compare performance to other classification methods. To further leverage our dynamic feature, we use the changes in network structure captured by motifs to help uncover real-world events using anomaly detection.
{"title":"Motif-Based Exploratory Data Analysis for State-Backed Platform Manipulation on Twitter","authors":"Khuzaima Hameed, Rob Johnston, Brent Younce, Minh Tang, Alyson Wilson","doi":"10.1609/icwsm.v17i1.22148","DOIUrl":"https://doi.org/10.1609/icwsm.v17i1.22148","url":null,"abstract":"State-backed platform manipulation (SBPM) on Twitter has been a prominent public issue since the 2016 US election cycle. Identifying and characterizing users on Twitter as belonging to a state-backed campaign is an important part of mitigating their influence. In this paper, we propose a novel time series feature grounded in social science to characterize dynamic user networks on Twitter. We introduce a classification approach, motif functional data analysis (MFDA), that captures the evolution of motifs in temporal networks, which is a useful feature for analyzing malign influence. We evaluate MFDA on data from known SBPM campaigns on Twitter and representative authentic data and compare performance to other classification methods. To further leverage our dynamic feature, we use the changes in network structure captured by motifs to help uncover real-world events using anomaly detection.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125992470","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}
Pub Date : 2023-06-02DOI: 10.1609/icwsm.v17i1.22227
Annika Marie Schoene, John E. Ortega, Silvio Amir, Kenneth Ward Church
This work starts with the TWISCO baseline, a benchmark of suicide-related content from Twitter. We find that hyper-parameter tuning can improve this baseline by 9%. We examined 576 combinations of hyper-parameters: learning rate, batch size, epochs and date range of training data. Reasonable settings of learning rate and batch size produce better results than poor settings. Date range is less conclusive. Balancing the date range of the training data to match the benchmark ought to improve performance, but the differences are relatively small. Optimal settings of learning rate and batch size are much better than poor settings, but optimal settings of date range are not that different from poor settings of date range. Finally, we end with concerns about reproducibility. Of the 576 experiments, 10% produced F1 performance above baseline. It is common practice in the literature to run many experiments and report the best, but doing so may be risky, especially given the sensitive nature of Suicide Ideation Detection.
{"title":"An Example of (Too Much) Hyper-Parameter Tuning In Suicide Ideation Detection","authors":"Annika Marie Schoene, John E. Ortega, Silvio Amir, Kenneth Ward Church","doi":"10.1609/icwsm.v17i1.22227","DOIUrl":"https://doi.org/10.1609/icwsm.v17i1.22227","url":null,"abstract":"This work starts with the TWISCO baseline, a benchmark of suicide-related content from Twitter. We find that hyper-parameter tuning can improve this baseline by 9%. We examined 576 combinations of hyper-parameters: learning rate, batch size, epochs and date range of training data. Reasonable settings of learning rate and batch size produce better results than poor settings. Date range is less conclusive. Balancing the date range of the training data to match the benchmark ought to improve performance, but the differences are relatively small. Optimal settings of learning rate and batch size are much better than poor settings, but optimal settings of date range are not that different from poor settings of date range. Finally, we end with concerns about reproducibility. Of the 576 experiments, 10% produced F1 performance above baseline. It is common practice in the literature to run many experiments and report the best, but doing so may be risky, especially given the sensitive nature of Suicide Ideation Detection.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124992256","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}
Pub Date : 2023-06-02DOI: 10.1609/icwsm.v17i1.22221
Ryan Harvey, R. Lebret, Stéphane Massonnet, K. Aberer, Gianluca Demartini
Social media is an important source of real-time imagery concerning world events. One subset of social media posts which may be of particular interest are those featuring firearms. These posts can give insight into weapon movements, troop activity and civilian safety. Object detection tools offer important opportunities for insight into these images. Unfortunately, these images can be visually complex, poorly lit and generally challenging for object detection models. We present an analysis of existing gun detection datasets, and find that these datasets to not effectively address the challenge of gun detection on real-life images. Following this, we present a novel object detection pipeline. We train our pipeline on a number of datasets including one created for this investigation made up of Twitter images of the Russo-Ukrainian War. We compare the performance of our model as trained on the different datasets to baseline numbers provided by original authors as well as a YOLO v5 benchmark. We find that our model outperforms the state-of-the-art benchmarks on contextually rich, real-life-derived imagery of firearms.
{"title":"Firearms on Twitter: A Novel Object Detection Pipeline","authors":"Ryan Harvey, R. Lebret, Stéphane Massonnet, K. Aberer, Gianluca Demartini","doi":"10.1609/icwsm.v17i1.22221","DOIUrl":"https://doi.org/10.1609/icwsm.v17i1.22221","url":null,"abstract":"Social media is an important source of real-time imagery concerning world events. One subset of social media posts which may be of particular interest are those featuring firearms. These posts can give insight into weapon movements, troop activity and civilian safety. Object detection tools offer important opportunities for insight into these images. Unfortunately, these images can be visually complex, poorly lit and generally challenging for object detection models. We present an analysis of existing gun detection datasets, and find that these datasets to not effectively address the challenge of gun detection on real-life images. Following this, we present a novel object detection pipeline. We train our pipeline on a number of datasets including one created for this investigation made up of Twitter images of the Russo-Ukrainian War. We compare the performance of our model as trained on the different datasets to baseline numbers provided by original authors as well as a YOLO v5 benchmark. We find that our model outperforms the state-of-the-art benchmarks on contextually rich, real-life-derived imagery of firearms.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132520914","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}
Pub Date : 2023-06-02DOI: 10.1609/icwsm.v17i1.22142
Jeffrey P. Gleason, Desheng Hu, Ronald E. Robertson, Christo Wilson
The contemporary Google Search Engine Results Page (SERP) supplements classic blue hyperlinks with complex components. These components produce tensions between searchers, 3rd-party websites, and Google itself over clicks and attention. In this study, we examine 12 SERP components from two categories: (1) extracted results (e.g., featured-snippets) and (2) Google Services (e.g., shopping-ads) to determine their effect on peoples’ behavior. We measure behavior with two variables: (1) click- through rate (CTR) to Google’s own domains versus 3rd-party domains and (2) time spent on the SERP. We apply causal inference methods to an ecologically valid trace dataset comprising 477,485 SERPs from 1,756 participants. We find that multiple components substantially increase CTR to Google domains, while others decrease CTR and increase time on the SERP. These findings may inform efforts to regulate the design of powerful intermediary platforms like Google.
{"title":"Google the Gatekeeper: How Search Components Affect Clicks and Attention","authors":"Jeffrey P. Gleason, Desheng Hu, Ronald E. Robertson, Christo Wilson","doi":"10.1609/icwsm.v17i1.22142","DOIUrl":"https://doi.org/10.1609/icwsm.v17i1.22142","url":null,"abstract":"The contemporary Google Search Engine Results Page (SERP) supplements classic blue hyperlinks with complex components. These components produce tensions between searchers, 3rd-party websites, and Google itself over clicks and attention. In this study, we examine 12 SERP components from two categories: (1) extracted results (e.g., featured-snippets) and (2) Google Services (e.g., shopping-ads) to determine their effect on peoples’ behavior. We measure behavior with two variables: (1) click- through rate (CTR) to Google’s own domains versus 3rd-party domains and (2) time spent on the SERP. We apply causal inference methods to an ecologically valid trace dataset comprising 477,485 SERPs from 1,756 participants. We find that multiple components substantially increase CTR to Google domains, while others decrease CTR and increase time on the SERP. These findings may inform efforts to regulate the design of powerful intermediary platforms like Google.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125992066","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}
Pub Date : 2023-06-02DOI: 10.1609/icwsm.v17i1.22132
Lin Chen, Fengli Xu, Qianyue Hao, Pan Hui, Yong Li
Understanding the impact of COVID-19 on urban life rhythms is crucial for accelerating the return-to-normal progress and envisioning more resilient and inclusive cities. While previous studies either depended on small-scale surveys or focused on the response to initial lockdowns, this paper uses large-scale location service data to systematically analyze the urban mobility behavior changes across three distinct phases of the pandemic, i.e., pre-pandemic, lockdown, and reopen. Our analyses reveal two typical patterns that govern the mobility behavior changes in most urban venues: daily life-centered urban venues go through smaller mobility drops during the lockdown and more rapid recovery after reopening, while work-centered urban venues suffer from more significant mobility drops that are likely to persist even after reopening. Such mobility behavior changes exert deeper impacts on the underlying social fabric, where the level of mobility reduction is positively correlated with the experienced segregation at that urban venue. Therefore, urban venues undergoing more mobility reduction are also more filled with people from homogeneous socio-demographic backgrounds. Moreover, mobility behavior changes display significant heterogeneity across geographical regions, which can be largely explained by the partisan inclination at the state level. Our study shows the vast potential of location service data in deriving a timely and comprehensive understanding of the social dynamic in urban space, which is valuable for informing the gradual transition back to the normal lifestyle in a “post-pandemic era”.
{"title":"Getting Back on Track: Understanding COVID-19 Impact on Urban Mobility and Segregation with Location Service Data","authors":"Lin Chen, Fengli Xu, Qianyue Hao, Pan Hui, Yong Li","doi":"10.1609/icwsm.v17i1.22132","DOIUrl":"https://doi.org/10.1609/icwsm.v17i1.22132","url":null,"abstract":"Understanding the impact of COVID-19 on urban life rhythms is crucial for accelerating the return-to-normal progress and envisioning more resilient and inclusive cities. While previous studies either depended on small-scale surveys or focused on the response to initial lockdowns, this paper uses large-scale location service data to systematically analyze the urban mobility behavior changes across three distinct phases of the pandemic, i.e., pre-pandemic, lockdown, and reopen. Our analyses reveal two typical patterns that govern the mobility behavior changes in most urban venues: daily life-centered urban venues go through smaller mobility drops during the lockdown and more rapid recovery after reopening, while work-centered urban venues suffer from more significant mobility drops that are likely to persist even after reopening. Such mobility behavior changes exert deeper impacts on the underlying social fabric, where the level of mobility reduction is positively correlated with the experienced segregation at that urban venue. Therefore, urban venues undergoing more mobility reduction are also more filled with people from homogeneous socio-demographic backgrounds. Moreover, mobility behavior changes display significant heterogeneity across geographical regions, which can be largely explained by the partisan inclination at the state level. Our study shows the vast potential of location service data in deriving a timely and comprehensive understanding of the social dynamic in urban space, which is valuable for informing the gradual transition back to the normal lifestyle in a “post-pandemic era”.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131296132","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}
Pub Date : 2023-06-02DOI: 10.1609/icwsm.v17i1.22128
Qimeng Cao, Qing Yin, Yunya Song, Zhihua Wang, Yujun Chen, R. Xu, Xian Yang
Network embedding is a process of encoding nodes into latent vectors by preserving network structure and content information. It is used in various applications, especially in recommender systems. In a social network setting, when recommending new friends to a user, the similarity between the user's embedding and the target friend will be examined. Traditional methods generate user node embedding without considering the recommendation target. No matter which target is to be recommended, the same embedding vector is generated for that particular user. This approach has its limitations. For example, a user can be both a computer scientist and a musician. When recommending music friends with potentially the same taste to him, we are interested in getting his representation that is useful in recommending music friends rather than computer scientists. His corresponding embedding should consider the user's musical features rather than those associated with computer science with the awareness that the recommendation targets are music friends. In order to address this issue, we propose a new framework which we name it as Recommendation Target-Aware Network embedding method (RTANet). Herein, the embedding of each user is no longer fixed to a constant vector, but it can vary according to their specific recommendation target. Concretely, RTANet assigns different attention weights to each neighbour node, allowing us to obtain the user's context information aggregated from its neighbours before transforming this context into its embedding. Different from other graph attention approaches, the attention weights in our work measure the similarity between each user's neighbour node and the target node, which in return generates the target-aware embedding. To demonstrate the effectiveness of our method, we compared RTANet with several state-of-the-art network embedding methods on four real-world datasets and showed that RTANet outperforms other comparative methods in the recommendation tasks.
{"title":"RTANet: Recommendation Target-Aware Network Embedding","authors":"Qimeng Cao, Qing Yin, Yunya Song, Zhihua Wang, Yujun Chen, R. Xu, Xian Yang","doi":"10.1609/icwsm.v17i1.22128","DOIUrl":"https://doi.org/10.1609/icwsm.v17i1.22128","url":null,"abstract":"Network embedding is a process of encoding nodes into latent vectors by preserving network structure and content information. It is used in various applications, especially in recommender systems. In a social network setting, when recommending new friends to a user, the similarity between the user's embedding and the target friend will be examined. Traditional methods generate user node embedding without considering the recommendation target. No matter which target is to be recommended, the same embedding vector is generated for that particular user. This approach has its limitations. For example, a user can be both a computer scientist and a musician. When recommending music friends with \u0000 potentially the same taste to him, we are interested in getting his representation that is useful in recommending music friends rather than computer scientists. His corresponding embedding should consider the user's musical features rather than those associated with computer science with the awareness that the recommendation targets are music friends. In order to address this issue, we propose a new framework which we name it as Recommendation Target-Aware Network embedding method (RTANet). Herein, the embedding of each user is no longer fixed to a constant vector, but it can vary according to their specific recommendation target. Concretely, RTANet assigns different attention weights to each neighbour node, allowing us to obtain the user's context information aggregated from its neighbours before transforming this context into its embedding. Different from other graph attention approaches, the attention weights in our work measure the similarity between each user's neighbour node and the target node, which in return generates the target-aware embedding. To demonstrate the effectiveness of our method, we compared RTANet with several state-of-the-art network embedding methods on \u0000four real-world datasets and showed that RTANet outperforms other comparative methods in the recommendation tasks.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114514467","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}
Pub Date : 2023-06-02DOI: 10.1609/icwsm.v17i1.22203
J. Ziegler, Michael Gertz
Trends are a fundamental component of today's fast-evolving media landscape. Still, a lot of questions about who participates in such trends remain unanswered. Are trends driven by individual actors, or do interactions between actors reveal community structures? If so, do those structures change during the life cycle of a trend or between topically similar trends? In short: Who is behind a trend? This paper contributes to a better understanding of these questions and, in general, actor networks underlying trends on social media. As a case study, we leverage a large Twitter dataset from the EURO2020 soccer competition to detect and analyze topical trends. Our novel Gaussian fitting method allows separating trend life cycles into up- and down-trend components, as well as determining the duration of trends. An event-based evaluation proves good performance results. Given separate trend stages and topically similar trends at different points in time, we conduct a temporal analysis of the actor networks during trends. Our findings not only reveal a large overlap of participants between successive trends but also indicate large variations within a trend life cycle. Furthermore, actor networks seem to be centred around a small number of dominant users and communities. Those users also show large stability across similar trends over time. In contrast, temporally stable community structures are neither found within nor across topically similar trends.
{"title":"Who Is behind a Trend? Temporal Analysis of Interactions among Trend Participants on Twitter","authors":"J. Ziegler, Michael Gertz","doi":"10.1609/icwsm.v17i1.22203","DOIUrl":"https://doi.org/10.1609/icwsm.v17i1.22203","url":null,"abstract":"Trends are a fundamental component of today's fast-evolving media landscape. Still, a lot of questions about who participates in such trends remain unanswered. Are trends driven by individual actors, or do interactions between actors reveal community structures? If so, do those structures change during the life cycle of a trend or between topically similar trends? In short: Who is behind a trend?\u0000This paper contributes to a better understanding of these questions and, in general, actor networks underlying trends on social media. As a case study, we leverage a large Twitter dataset from the EURO2020 soccer competition to detect and analyze topical trends. Our novel Gaussian fitting method allows separating trend life cycles into up- and down-trend components, as well as determining the duration of trends. An event-based evaluation proves good performance results. Given separate trend stages and topically similar trends at different points in time, we conduct a temporal analysis of the actor networks during trends. Our findings not only reveal a large overlap of participants between successive trends but also indicate large variations within a trend life cycle. Furthermore, actor networks seem to be centred around a small number of dominant users and communities. Those users also show large stability across similar trends over time. In contrast, temporally stable community structures are neither found within nor across topically similar trends.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116250379","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}
Pub Date : 2023-06-02DOI: 10.1609/icwsm.v17i1.22135
Xi Ding, Michael A. Horning, E. H. Rho
With the growth of online news over the past decade, empirical studies on political discourse and news consumption have focused on the phenomenon of filter bubbles and echo chambers. Yet recently, scholars have revealed limited evidence around the impact of such phenomenon, leading some to argue that partisan segregation across news audiences can- not be fully explained by online news consumption alone and that the role of traditional legacy media may be as salient in polarizing public discourse around current events. In this work, we expand the scope of analysis to include both online and more traditional media by investigating the relationship between broadcast news media language and social media discourse. By analyzing a decade’s worth of closed captions (2.1 million speaker turns) from CNN and Fox News along with topically corresponding discourse from Twitter, we pro- vide a novel framework for measuring semantic polarization between America’s two major broadcast networks to demonstrate how semantic polarization between these outlets has evolved (Study 1), peaked (Study 2) and influenced partisan discussions on Twitter (Study 3) across the last decade. Our results demonstrate a sharp increase in polarization in how topically important keywords are discussed between the two channels, especially after 2016, with overall highest peaks occurring in 2020. The two stations discuss identical topics in drastically distinct contexts in 2020, to the extent that there is barely any linguistic overlap in how identical keywords are contextually discussed. Further, we demonstrate at-scale, how such partisan division in broadcast media language significantly shapes semantic polarity trends on Twitter (and vice-versa), empirically linking for the first time, how online discussions are influenced by televised media. We show how the language characterizing opposing media narratives about similar news events on TV can increase levels of partisan dis- course online. To this end, our work has implications for how media polarization on TV plays a significant role in impeding rather than supporting online democratic discourse.
{"title":"Same Words, Different Meanings: Semantic Polarization in Broadcast Media Language Forecasts Polarity in Online Public Discourse","authors":"Xi Ding, Michael A. Horning, E. H. Rho","doi":"10.1609/icwsm.v17i1.22135","DOIUrl":"https://doi.org/10.1609/icwsm.v17i1.22135","url":null,"abstract":"With the growth of online news over the past decade, empirical studies on political discourse and news consumption have focused on the phenomenon of filter bubbles and echo chambers. Yet recently, scholars have revealed limited evidence around the impact of such phenomenon, leading some to argue that partisan segregation across news audiences can- not be fully explained by online news consumption alone and that the role of traditional legacy media may be as salient in polarizing public discourse around current events. In this work, we expand the scope of analysis to include both online and more traditional media by investigating the relationship between broadcast news media language and social media discourse. By analyzing a decade’s worth of closed captions (2.1 million speaker turns) from CNN and Fox News along with topically corresponding discourse from Twitter, we pro- vide a novel framework for measuring semantic polarization between America’s two major broadcast networks to demonstrate how semantic polarization between these outlets has evolved (Study 1), peaked (Study 2) and influenced partisan discussions on Twitter (Study 3) across the last decade. Our results demonstrate a sharp increase in polarization in how topically important keywords are discussed between the two channels, especially after 2016, with overall highest peaks occurring in 2020. The two stations discuss identical topics in drastically distinct contexts in 2020, to the extent that there is barely any linguistic overlap in how identical keywords are contextually discussed. Further, we demonstrate at-scale, how such partisan division in broadcast media language significantly shapes semantic polarity trends on Twitter (and vice-versa), empirically linking for the first time, how online discussions are influenced by televised media. We show how the language characterizing opposing media narratives about similar news events on TV can increase levels of partisan dis- course online. To this end, our work has implications for how media polarization on TV plays a significant role in impeding rather than supporting online democratic discourse.","PeriodicalId":175641,"journal":{"name":"International Conference on Web and Social Media","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124002073","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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