Social media provide a unique opportunity for charities to reach a large audience with whom they can engage in productive two-way conversations. This abstract reports findings from a study that seeks to determine the extent to which these conversations occur, and whether they differ between Facebook and Twitter. Differences arise showing that Facebook receives more conversations in response to the charities' own posts. However, on Twitter more comments are made per each engaged supporter, which could represent more unsolicited discussion that provides an alternative type of value.
{"title":"Taking the relationship to the next level: a comparison of how supporters converse with charities on facebook and twitter","authors":"Christopher Phethean, T. Tiropanis, L. Harris","doi":"10.1145/2615569.2615648","DOIUrl":"https://doi.org/10.1145/2615569.2615648","url":null,"abstract":"Social media provide a unique opportunity for charities to reach a large audience with whom they can engage in productive two-way conversations. This abstract reports findings from a study that seeks to determine the extent to which these conversations occur, and whether they differ between Facebook and Twitter. Differences arise showing that Facebook receives more conversations in response to the charities' own posts. However, on Twitter more comments are made per each engaged supporter, which could represent more unsolicited discussion that provides an alternative type of value.","PeriodicalId":93136,"journal":{"name":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","volume":"6 1","pages":"271-272"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85891740","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}
J. Vigil, Asa Shumskas Tait, C. Wienberg, A. Gordon
"Friends You Haven't Met Yet" is a documentary short film that chronicles encounters between extremely prolific bloggers and a computer scientist who uses their personal narratives for research. It explores issues related to public sharing of personal stories, the ethical obligations of researchers who use web data, and the changing nature of online privacy. The film was conceived by Andrew Gordon and Christopher Wienberg at the University of Southern California, whose research involves the collection of millions of personal stories posted to internet weblogs. In analyzing their data, these researchers discovered an unusual population of extremely prolific bloggers, people who post personal stories about their daily lives everyday over the course of many years. They posed three questions about this population: 1. What motivates these people to post so frequently and publicly about their personal life? 2. To what degree do these people embellish their stories to make them more interesting than reality? 3. What expectations do these authors have about their readers, and what are the ethical implications for researchers like us who analyze their posts? To answer these questions, PhD Student Christopher Wienberg contacted many of these bloggers directly and set up face-to-face interviews at their homes. Accompanied by a documentary film crew, Christopher traveled to locations around California, in both urban and rural settings, to better understand the people whose contributions on the web serve as data in social media research.
{"title":"Friends you haven't met yet: a documentary short film","authors":"J. Vigil, Asa Shumskas Tait, C. Wienberg, A. Gordon","doi":"10.1145/2615569.2617797","DOIUrl":"https://doi.org/10.1145/2615569.2617797","url":null,"abstract":"\"Friends You Haven't Met Yet\" is a documentary short film that chronicles encounters between extremely prolific bloggers and a computer scientist who uses their personal narratives for research. It explores issues related to public sharing of personal stories, the ethical obligations of researchers who use web data, and the changing nature of online privacy. The film was conceived by Andrew Gordon and Christopher Wienberg at the University of Southern California, whose research involves the collection of millions of personal stories posted to internet weblogs. In analyzing their data, these researchers discovered an unusual population of extremely prolific bloggers, people who post personal stories about their daily lives everyday over the course of many years. They posed three questions about this population: 1. What motivates these people to post so frequently and publicly about their personal life? 2. To what degree do these people embellish their stories to make them more interesting than reality? 3. What expectations do these authors have about their readers, and what are the ethical implications for researchers like us who analyze their posts? To answer these questions, PhD Student Christopher Wienberg contacted many of these bloggers directly and set up face-to-face interviews at their homes. Accompanied by a documentary film crew, Christopher traveled to locations around California, in both urban and rural settings, to better understand the people whose contributions on the web serve as data in social media research.","PeriodicalId":93136,"journal":{"name":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","volume":"422 1","pages":"176"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77504181","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}
Jiejun Xu, Ryan Compton, Tsai-Ching Lu, David Allen
Tumblr, a microblogging platform and social media website, has been gaining popularity over the past few years. Despite its success, little has been studied on the human behavior and interaction on this platform. This is important as it sheds light on the driving force behind Tumblr's growth. In this work, we present a quantitative study of Tumblr based on the complete data coverage for four consecutive months consisting of 23.2 million users and 10.2 billion posts. We first explore various attributes of users, posts, and tags in detail and extract behavioral patterns based on the user generated content. We then construct a massive reblog network based on the primary user interactions on Tumblr and present findings on analyzing its topological structure and properties. Finally, we show substantial results on providing location-specific usage patterns from Tumblr, despite no built-in support for geo-tagging or user location functionality. Essentially this is done by conducting a large-scale user alignment with a different social media platform (e.g., Twitter) and subsequently propagating geo-information across platforms. To the best of our knowledge, this work is the first attempt to carry out large-scale measurement-driven analysis on Tumblr.
{"title":"Rolling through tumblr: characterizing behavioral patterns of the microblogging platform","authors":"Jiejun Xu, Ryan Compton, Tsai-Ching Lu, David Allen","doi":"10.1145/2615569.2615694","DOIUrl":"https://doi.org/10.1145/2615569.2615694","url":null,"abstract":"Tumblr, a microblogging platform and social media website, has been gaining popularity over the past few years. Despite its success, little has been studied on the human behavior and interaction on this platform. This is important as it sheds light on the driving force behind Tumblr's growth. In this work, we present a quantitative study of Tumblr based on the complete data coverage for four consecutive months consisting of 23.2 million users and 10.2 billion posts. We first explore various attributes of users, posts, and tags in detail and extract behavioral patterns based on the user generated content. We then construct a massive reblog network based on the primary user interactions on Tumblr and present findings on analyzing its topological structure and properties. Finally, we show substantial results on providing location-specific usage patterns from Tumblr, despite no built-in support for geo-tagging or user location functionality. Essentially this is done by conducting a large-scale user alignment with a different social media platform (e.g., Twitter) and subsequently propagating geo-information across platforms. To the best of our knowledge, this work is the first attempt to carry out large-scale measurement-driven analysis on Tumblr.","PeriodicalId":93136,"journal":{"name":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","volume":"1 1","pages":"13-22"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81213867","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 explosion in social media adoption has opened up new opportunities to understand human interaction and information flow at an unprecedented scale. Influence between people represented as nodes of a social graph is best characterized in terms of the direction, the volume and the delay associated with the information flow. In this work we investigate the relatively new information-theoretic measure called transfer entropy as a measure of directed causal influence in online social interactions. The classical definition of transfer entropy is extended to a form applicable to activity on social graphs characterized by causal influence through delayed responses. For fixed but arbitrary interaction delays, we show that the swept delayed transfer entropy (DTE) profile peaks at the true delay. By extending the results to discrete and continuous distributions of interaction delays, the efficacy of DTE in recovering the interaction delay distributions between two causally related signals is demonstrated. An information theoretic annotation of social graphs that captures the volume and velocity of information transfer is presented based on the swept DTE.
{"title":"An activity-based information-theoretic annotation of social graphs","authors":"A. Sathanur, V. Jandhyala","doi":"10.1145/2615569.2615673","DOIUrl":"https://doi.org/10.1145/2615569.2615673","url":null,"abstract":"The explosion in social media adoption has opened up new opportunities to understand human interaction and information flow at an unprecedented scale. Influence between people represented as nodes of a social graph is best characterized in terms of the direction, the volume and the delay associated with the information flow. In this work we investigate the relatively new information-theoretic measure called transfer entropy as a measure of directed causal influence in online social interactions. The classical definition of transfer entropy is extended to a form applicable to activity on social graphs characterized by causal influence through delayed responses. For fixed but arbitrary interaction delays, we show that the swept delayed transfer entropy (DTE) profile peaks at the true delay. By extending the results to discrete and continuous distributions of interaction delays, the efficacy of DTE in recovering the interaction delay distributions between two causally related signals is demonstrated. An information theoretic annotation of social graphs that captures the volume and velocity of information transfer is presented based on the swept DTE.","PeriodicalId":93136,"journal":{"name":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","volume":"1 1","pages":"187-191"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87125131","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}
We present a cross-modal recommendation engine that leverages multiple domains of data while performing matrix factorization. We show how our approach has the potential to alleviate the cold-start problem for new items, one of the notorious limitations of Collaborative Filtering (CF) techniques.
{"title":"A cross-modal warm-up solution for the cold-start problem in collaborative filtering recommender systems","authors":"B. Abdollahi, O. Nasraoui","doi":"10.1145/2615569.2615665","DOIUrl":"https://doi.org/10.1145/2615569.2615665","url":null,"abstract":"We present a cross-modal recommendation engine that leverages multiple domains of data while performing matrix factorization. We show how our approach has the potential to alleviate the cold-start problem for new items, one of the notorious limitations of Collaborative Filtering (CF) techniques.","PeriodicalId":93136,"journal":{"name":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","volume":"69 1","pages":"257-258"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85939971","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}
Research on Information Warfare on the Web is still at an early stage and the question of the true nature of Cyberwarfare actions that target the Web needs to be answered on both conceptual and methodological levels. Existing research proved that the Web is a new battlefield with specific strategic objectives, but research needs to create assessment tools to validate the impact of Cyberattacks, especially when they aim at "soft" targets like Web sites or Social Media platforms. This position paper serves as starting point of reference and discussion and wants to clarify several misunderstandings in definitions of Information Warfare and Cyberwarfare. It also offers methodological directions to identify actions and measure their impact to answer the question: Is Cyberwarfare just a weapon of mass annoyance?
{"title":"Infowar on the web: measuring mass annoyance","authors":"S. Bazan, Sabrine Saad, Addis Tesfa","doi":"10.1145/2615569.2615664","DOIUrl":"https://doi.org/10.1145/2615569.2615664","url":null,"abstract":"Research on Information Warfare on the Web is still at an early stage and the question of the true nature of Cyberwarfare actions that target the Web needs to be answered on both conceptual and methodological levels. Existing research proved that the Web is a new battlefield with specific strategic objectives, but research needs to create assessment tools to validate the impact of Cyberattacks, especially when they aim at \"soft\" targets like Web sites or Social Media platforms. This position paper serves as starting point of reference and discussion and wants to clarify several misunderstandings in definitions of Information Warfare and Cyberwarfare. It also offers methodological directions to identify actions and measure their impact to answer the question: Is Cyberwarfare just a weapon of mass annoyance?","PeriodicalId":93136,"journal":{"name":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","volume":"69 1","pages":"283-284"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85455955","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}
We present a study about gender differences towards anthropogenic impact on climate change, as discovered from the climate change debate on Twitter. Our dataset consists of about 250,000 tweets and retweets for which the authors' gender was identified. We researched the hashtags and usernames that were proportionately more frequently mentioned by either male or female tweeters. Our results show significant differences between female and male tweeters, with female tweeters mentioning significantly more campaigns and organizations with a convinced attitude towards anthropogenic impact on climate change, and male tweeters mentioning significantly more private persons and usernames with a sceptical stance.
{"title":"Analyzing the climate change debate on Twitter: content and differences between genders","authors":"K. Holmberg, I. Hellsten","doi":"10.1145/2615569.2615638","DOIUrl":"https://doi.org/10.1145/2615569.2615638","url":null,"abstract":"We present a study about gender differences towards anthropogenic impact on climate change, as discovered from the climate change debate on Twitter. Our dataset consists of about 250,000 tweets and retweets for which the authors' gender was identified. We researched the hashtags and usernames that were proportionately more frequently mentioned by either male or female tweeters. Our results show significant differences between female and male tweeters, with female tweeters mentioning significantly more campaigns and organizations with a convinced attitude towards anthropogenic impact on climate change, and male tweeters mentioning significantly more private persons and usernames with a sceptical stance.","PeriodicalId":93136,"journal":{"name":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","volume":"1 1","pages":"287-288"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78008987","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}
We introduce a method that can measure the degree of regularity or irregularity of the behavior for enhancing the performance of location-based services (LBSs) such as check-in. It is still challenging for LBSs to determine the places to recommend that best suits the user's needs. Our aim is to identify the user's status (regular or irregular) of each check-in. Most previous studies approached this problem by acquiring usual locations (e.g., home or office) or assessing check-in frequency. We propose more effective measure by using a multinomial-distribution-based method that considers the periodic check-ins of the user on various time-scales. Our method can accurately identify irregular check-ins even in usual locations and we find that the users tend to continue irregular check-ins in a certain range of time.
{"title":"Regular behavior measure for location based services","authors":"Aki Hayashi, T. Matsubayashi, H. Sawada","doi":"10.1145/2615569.2615657","DOIUrl":"https://doi.org/10.1145/2615569.2615657","url":null,"abstract":"We introduce a method that can measure the degree of regularity or irregularity of the behavior for enhancing the performance of location-based services (LBSs) such as check-in. It is still challenging for LBSs to determine the places to recommend that best suits the user's needs. Our aim is to identify the user's status (regular or irregular) of each check-in. Most previous studies approached this problem by acquiring usual locations (e.g., home or office) or assessing check-in frequency. We propose more effective measure by using a multinomial-distribution-based method that considers the periodic check-ins of the user on various time-scales. Our method can accurately identify irregular check-ins even in usual locations and we find that the users tend to continue irregular check-ins in a certain range of time.","PeriodicalId":93136,"journal":{"name":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","volume":"105 1","pages":"299-300"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75369319","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}
Ramine Tinati, Markus Luczak-Rösch, E. Simperl, N. Shadbolt
{"title":"Motivations of citizen scientists: a quantitative investigation of forum participation","authors":"Ramine Tinati, Markus Luczak-Rösch, E. Simperl, N. Shadbolt","doi":"10.1145/2615569.2615651","DOIUrl":"https://doi.org/10.1145/2615569.2615651","url":null,"abstract":"","PeriodicalId":93136,"journal":{"name":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","volume":"22 1","pages":"295-296"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73812860","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}
Passive surveillance of preferences, opinions and behaviors on social media is becoming increasingly common. The general goal is to make inferences from observations collected from the numerous posts publicly available in blogs, microblogs, and other social forums. A traditional approach for collecting observations is by querying a random (or convenience) sample of individuals with surveys. A wide variety of well respected survey instruments have been developed over many decades especially in social sciences.The question addressed here is: how does one `translate' a survey of interest into surveillance strategies on social media? Specifically, how does one find the posts that could be interpreted as valid responses to the survey? Developing a general methodology for translating a survey into social medial surveillance might further the inclusion of social media research into traditional social science research. We propose a translation methodology using a well-reputed survey (the Satisfaction with Life Scale) as an example. A second methodological contribution that goes beyond the survey translation focus is a crowdsourcing approach, which we claim with reasonable confidence, finds close to ul{all} the relevant items in a dataset. This is different from the standard approach of asking workers to annotate all items in a small dataset. Our method supports more accurate evaluations (i.e., more precise recall calculations) as well as the development of larger training datasets. Finally the resulting surveillance method derived from the life satisfaction survey achieves recall, precision and F scores between 0.59 and 0.65. This is considerably better than standard methods using lexicons (precision around 0.16) or classifiers (precision, recall and F scores between 0.32 and 0.38).
{"title":"Translating surveys to surveillance on social media: methodological challenges & solutions","authors":"Chao Yang, P. Srinivasan","doi":"10.1145/2615569.2615696","DOIUrl":"https://doi.org/10.1145/2615569.2615696","url":null,"abstract":"Passive surveillance of preferences, opinions and behaviors on social media is becoming increasingly common. The general goal is to make inferences from observations collected from the numerous posts publicly available in blogs, microblogs, and other social forums. A traditional approach for collecting observations is by querying a random (or convenience) sample of individuals with surveys. A wide variety of well respected survey instruments have been developed over many decades especially in social sciences.The question addressed here is: how does one `translate' a survey of interest into surveillance strategies on social media? Specifically, how does one find the posts that could be interpreted as valid responses to the survey? Developing a general methodology for translating a survey into social medial surveillance might further the inclusion of social media research into traditional social science research. We propose a translation methodology using a well-reputed survey (the Satisfaction with Life Scale) as an example. A second methodological contribution that goes beyond the survey translation focus is a crowdsourcing approach, which we claim with reasonable confidence, finds close to ul{all} the relevant items in a dataset. This is different from the standard approach of asking workers to annotate all items in a small dataset. Our method supports more accurate evaluations (i.e., more precise recall calculations) as well as the development of larger training datasets. Finally the resulting surveillance method derived from the life satisfaction survey achieves recall, precision and F scores between 0.59 and 0.65. This is considerably better than standard methods using lexicons (precision around 0.16) or classifiers (precision, recall and F scores between 0.32 and 0.38).","PeriodicalId":93136,"journal":{"name":"Proceedings of the ... ACM Web Science Conference. ACM Web Science Conference","volume":"32 1","pages":"4-12"},"PeriodicalIF":0.0,"publicationDate":"2014-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74566954","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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