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Twitter in Academic Conferences: Usage, Networking and Participation over Time
"... Twitter is often referred to as a backchannel for conferences. While the main conference takes place in a physical setting, attendees and virtual attendees socialize, introduce new ideas or broadcast information by microblogging on Twitter. In this paper we ana-lyze the scholars ’ Twitter use in 16 ..."
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Twitter is often referred to as a backchannel for conferences. While the main conference takes place in a physical setting, attendees and virtual attendees socialize, introduce new ideas or broadcast information by microblogging on Twitter. In this paper we ana-lyze the scholars ’ Twitter use in 16 Computer Science conferences over a timespan of five years. Our primary finding is that over the years there are increasing differences with respect to conversation use and information use in Twitter. We studied the interaction net-work between users to understand whether assumptions about the structure of the conversations hold over time and between differ-ent types of interactions, such as retweets, replies, and mentions. While ‘people come and people go’, we want to understand what keeps people stay with the conference on Twitter. By casting the problem to a classification task, we find different factors that con-tribute to the continuing participation of users to the online Twitter conference activity. These results have implications for research communities to implement strategies for continuous and active par-ticipation among members.
1Episogram: Visual Summarization of Egocentric Social Interactions
"... Abstract—Visualizing social interaction data has been of booming interest as the recent availability of social traces, ranging from the conversations left in social media to groups ’ collaborations archived in publications. The key challenges of visualizing social interaction data including the diff ..."
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Abstract—Visualizing social interaction data has been of booming interest as the recent availability of social traces, ranging from the conversations left in social media to groups ’ collaborations archived in publications. The key challenges of visualizing social interaction data including the difficulties of (1) understanding the general structure of social interactions and (2) representing the data in the context of different user activities for revealing different behavior patterns. In this paper, we present, Episogram, for visualizing social interaction data. Our design is based on an anatomy of social interaction process in which the actors and objects involved can be formally represented as a time-varying tripartite network. In Episogram, we display and aggregate such tripartite networks along multiple temporal dimensions, from different actors ’ egocentric perspectives. We show the effectiveness of the proposed technique via case studies and user studies. The results indicate that our design provides non-trivial insights from social interaction data.
Climate change on Twitter: Topics, communities and conversations about the 2013 IPCCWorking Group 1 Report
- PLoS ONE 9
, 2014
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Social Media Dynamics of Global Co-presence During the 2014 FIFA World Cup
"... Sports games and other media events can induce very strong feelings of co-presence that can change communi-cation patterns within large communities. Live tweeting reactions to media events provide high-resolution data with time-stamps to understand these behavioral dy-namics. We employ a computation ..."
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Sports games and other media events can induce very strong feelings of co-presence that can change communi-cation patterns within large communities. Live tweeting reactions to media events provide high-resolution data with time-stamps to understand these behavioral dy-namics. We employ a computational focus group method to identify 790,744 international Twitter users, and we track their behavior before and during the 2014 FIFA World Cup. We pick a set of Twitter users who spec-ified the teams that they are supporting, such that we can identify communities of fans of the teams, as well as the entire community of World Cup fans. The structure, dynamics, and content of communication of these com-munities are analyzed to compare behavior outside and during the event and to examine behavioral responses across languages. Specifically, the temporal patterns of the tweeting volume, topics, retweeting, and mention-ing behaviors are analyzed. We find similarities in the responses to media events, characteristic changes in ac-tivity patterns, and substantial differences in linguistic features. Our findings have implications for designing more resilient socio-technical systems during crises and developing better models of complex social behavior.
RESEARCH ARTICLE Timescales of Massive Human Entrainment
"... The past two decades have seen an upsurge of interest in the collective behaviors of com-plex systems composed of many agents entrained to each other and to external events. In this paper, we extend the concept of entrainment to the dynamics of human collective atten-tion. We conducted a detailed in ..."
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The past two decades have seen an upsurge of interest in the collective behaviors of com-plex systems composed of many agents entrained to each other and to external events. In this paper, we extend the concept of entrainment to the dynamics of human collective atten-tion. We conducted a detailed investigation of the unfolding of human entrainment—as ex-pressed by the content and patterns of hundreds of thousands of messages on Twitter— during the 2012 US presidential debates. By time-locking these data sources, we quantify the impact of the unfolding debate on human attention at three time scales. We show that collective social behavior covaries second-by-second to the interactional dynamics of the debates: A candidate speaking induces rapid increases in mentions of his name on social media and decreases in mentions of the other candidate. Moreover, interruptions by an in-terlocutor increase the attention received. We also highlight a distinct time scale for the im-pact of salient content during the debates: Across well-known remarks in each debate, mentions in social media start within 5–10 seconds after it occurs; peak at approximately one minute; and slowly decay in a consistent fashion across well-known events during the debates. Finally, we show that public attention after an initial burst slowly decays through the course of the debates. Thus we demonstrate that large-scale human entrainment may hold across a number of distinct scales, in an exquisitely time-locked fashion. The methods and results pave the way for careful study of the dynamics and mechanisms of large-scale human entrainment.
Twitter in Academic Events: A Study of Temporal Usage, Communication, Sentimental and Topical Patterns in 16 Computer Science Conferences
"... Twitter is often referred to as a backchannel for conferences. While the main conference takes place in a physical setting, on-site and off-site attendees socialize, introduce new ideas or broadcast information by microblogging on Twitter. In this paper we analyze scholars ’ Twitter usage in 16 Comp ..."
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Twitter is often referred to as a backchannel for conferences. While the main conference takes place in a physical setting, on-site and off-site attendees socialize, introduce new ideas or broadcast information by microblogging on Twitter. In this paper we analyze scholars ’ Twitter usage in 16 Computer Science conferences over a timespan of five years. Our primary finding is that over the years there are differences with respect to the uses of Twitter, with an increase of informational activity (retweets and URLs), and a decrease of conversational usage (replies and mentions), which also impacts the network structure – meaning the amount of connected components – of the informational and conversational networks. We also applied topic modeling over the tweets ’ content and found that when clustering conferences according to their topics the resulting dendrogram clearly reveals the similarities and differences of the actual research interests of those events. Furthermore, we also analyzed the sentiment of tweets and found persistent differences among conferences. It also shows that some communities consistently express messages with higher levels of emotions while others do it in a more neutral manner. Finally, we investigated some features that can help predict future user participation in the online Twitter conference activity. By casting the problem as a classification task, we created a model that identifies factors that contribute to the continuing user participation. Our results have implications for research communities to implement strategies for continuous and active participation among members. Moreover, our work reveals the potential for the use
RESEARCH ARTICLE Climate Change Sentiment on Twitter: An Unsolicited Public Opinion Poll
"... The consequences of anthropogenic climate change are extensively debated through sci-entific papers, newspaper articles, and blogs. Newspaper articles may lack accuracy, while the severity of findings in scientific papers may be too opaque for the public to understand. Social media, however, is a fo ..."
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The consequences of anthropogenic climate change are extensively debated through sci-entific papers, newspaper articles, and blogs. Newspaper articles may lack accuracy, while the severity of findings in scientific papers may be too opaque for the public to understand. Social media, however, is a forum where individuals of diverse backgrounds can share their thoughts and opinions. As consumption shifts from old media to new, Twitter has become a valuable resource for analyzing current events and headline news. In this research, we ana-lyze tweets containing the word “climate ” collected between September 2008 and July 2014. Through use of a previously developed sentiment measurement tool called the Hedonometer, we determine how collective sentiment varies in response to climate change news, events, and natural disasters. We find that natural disasters, climate bills, and oil-drilling can contribute to a decrease in happiness while climate rallies, a book release, and a green ideas contest can contribute to an increase in happiness. Words uncovered by our analysis suggest that responses to climate change news are predominately from climate change activists rather than climate change deniers, indicating that Twitter is a valuable resource for the spread of climate change awareness.
Anticipating Activity in Social Media Spikes∗
, 2014
"... We propose a novel mathematical model for the activity of microblog-gers during an external, event-driven spike. The model leads to a testable prediction of who would become most active if a spike were to take place. This type of information is of great interest to commercial organisations, governme ..."
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We propose a novel mathematical model for the activity of microblog-gers during an external, event-driven spike. The model leads to a testable prediction of who would become most active if a spike were to take place. This type of information is of great interest to commercial organisations, governments and charities, as it identifies key players who can be targeted with information in real time when the network is most receptive. The model takes account of the fact that dynamic interactions evolve over an underlying, static network that records “who listens to whom. ” The model is based on the assumption that, in the case where the entire community has become aware of an external news event, a key driver of activity is the motivation to participate by responding to incoming messages. We test the model on a large scale Twitter conversation concerning the appointment of a UK Premier League football club manager. We also present further results for a Bundesliga football match, a marketing event and a television programme. In each case we find that exploiting the underlying connectiv-ity structure improves the prediction of who will be active during a spike. We also show how the half-life of a spike in activity can be quantified in terms of the network size and the typical response rate.
