In conjunction with THE 11TH INTERNATIONAL AAAI CONFERENCE ON WEB AND SOCIAL MEDIA (ICWSM-17)
May 15th 2017, Montreal, Canada
Social media channels enjoy many advantages over traditional media channels, such as ubiquity, mobility, immediacy, and seamless communication in reporting, covering and sharing real-world events, e.g., the Boston bombings, the NBA finals, and the U.S Presidential elections. Given these advantages, social media posts such as tweets can typically reflect events as they happen, in real-time. Despite these benefits, social media channels also tend to be noisy, chaotic, and overwhelming. As a result, the vast amount of noisy social media data poses tremendous challenges for conducting in-depth analysis, which is critical to applications for event playback, journalistic investigation, storytelling, etc. The purpose of this half-day workshop is to bring together researchers that are working in a variety of areas that are all related to the larger problem of analyzing and understanding events using social media responses, to discuss: 1) what are the recently developed machine learning and data mining techniques that can be leveraged to address challenges in analyzing events using social media data, and 2) from challenges in analyzing events, what are the practical research directions in the machine learning and data mining community.
Topics of Interest
We encourage submissions on a variety of topics, including but not limited to:
EASM 2017 invites contributions focused on all aspects of event analytics using social media data. We welcome papers that discuss new challenges and potential solutions and encourage submissions which present early stages of cutting-edge research and development. We accept regular technical papers (6 pages), extended abstracts (2 pages), and position papers (1 page). The presentation format will be determined by the paper quality, innovation, etc. Accepted works will be posted online in early May 2017. All submissions are single-blind
Please submit your contribution to: email@example.comWe invite contributions focused on all aspects of event analytics using social media data. We welcome position papers that discuss new challenges and potential solutions and encourage submissions which present early stages of cutting-edge research and development. For both presentation formats, please submit an extended abstract, up to two pages in length, using the AAAI format. Accepted abstracts will be posted online in early May 2017. >>>>>>> Stashed changes
Yuheng Hu Email is an assistant professor at University of Illinois at Chicago. Yuheng works at the interface of Social Computing, HCI and Machine Learning. His research focuses on developing algorithms, tools and systems to characterize, make sense of, and predict people's reactions on social media in response to different real world events. His work has been published at various highly reputed conferences including AAAI, IJCAI, ICWSM and CHI, where he won a best paper nomination in 2013. His work has also been featured in press outlets such as ABC, PBS, The Seattle Times, and FastCompany.
Yu-Ru Lin Email is an assistant professor at the School of Information Sciences, University of Pittsburgh. Her research interests include human mobility, social and political network dynamics, and computational social science. She has developed computational approaches for mining and visualizing large-scale, time-varying, heterogeneous, multi-relational, and semi-structured data. Her current research focuses on extracting system-level features from big data sets, including social media data and anonymized cellphone records, for studying human and social dynamics, particularly under exogenous events such as emergencies and media events. Her work has appeared in prestigious scientific venues including WWW, SIGKDD, InfoVis, ACM TKDD, ACM TOMCCAP, IEEEP and PLoS ONE.
Cheng Chen Email Cheng Chen is a Ph.D. candidate at the Department of Information and Decision Sciences, University of Illinois at Chicago. Prior to that, he received his Master of Statistics at Rutgers University. His research interests involve developing machine learning algorithms and applying statistical methods to characterize how social media users respond to real world events. His recent research on the reaction of Super Bowl audience towards commercial ads has been accepted and will be published at ICWSM..
|8:30 - 8:40||Welcome Message|
|8:40 - 9:00||Meet workshop participantsAllow all participants to introduce themselves, their research interests and their interests in the topic of the workshop.|
|9:00 - 9:25||Analyzing the Voices during European Migrant Crisis in Blogosphere|
Muhammad Nihal Hussain, Kiran Bandeli, Mohammad Nooman, Samer Al-khateeb, Nitin Agarwal
Abstract: Social media has grown to be the place for voicing one’s opinions and share interests and information freely with others. Individuals use this as a platform to exchange thoughts with others on various activities ranging from coordinating cyber campaigns to bringing awareness for diseases or disorders by online health communities. Almost all events, issues, crises around the world, nowadays, are discussed on social media, and often even break out on social media channels such as Twitter, Facebook, and Blogs. Blog is a platform that has no restriction on the number of characters, which allows people to express their opinions, develop narratives, and discuss events in greater depths. Typically, other social media platforms like Twitter and Facebook serve as vehicles that drive audience to the blogs and enable the discussions. Blogs provide a rich medium for content analysis to gain situational awareness about the events. However, due to the difficulty in collecting blog data and the lack of blog tracking tools, analyzing blogs is challenging. In this paper, we will use Blogtrackers (which is a blog tracking tool designed to explore the blogosphere and to gain insights on events around the world) to study and analyze the migrant crisis in Europe. The analysis helps us in identifying leading information actors, influential bloggers, popular domains and URLs, trending keywords, and an overall shift in the narratives, i.e., from a pro-refugee to an anti-refugee sentiment.
|9:25 - 9:50||Leveraging Causal Relationships to Predict Dynamics of Domain-related Events|
Abstract: Besides tracking dynamics of public events (e.g., political events) from social media signals, it is also important to track dynamics of domain-related events relevant to particular business cases (such as anomaly decrease of attention to a product). eMentalist Connect project deals with development of a framework for detecting domain-related events and predicting dynamics of these events considering causalities relationships, extracted from social media signals. This position paper discusses challenges and primarily results of this project.
|9:50 - 10:30||Breakout GroupUsing pre-sorted groups (based on the submissions), organizer "fire-starters" will work with participants to identify tractable entry points for studying grand challenges. (e.g., "what are the fundermantal problems in studying event using social media data?").|
|10:30 - 10:45||Coffee break|
|10:45 - 11:10||Mood Congruence or Mood Consistency? Examining Aggregated Twitter Sentiment towards Ads in 2016 Super Bowl|
Tingting Nian, Yuheng Hu, Cheng Chen
Abstract: While the popularity of Social TV has recently attracted sig- nificant research efforts from various research communities, little is known about how viewers’ Twitter sentiment toward TV advertising is affected by the viewers’ mood state affected by the TV program. In this paper, we take a large set of tweets posted during the Super Bowl 2016 to investigate the effect of audience’s mood induced by the game on their reactions the Super Bowl commercials. Our results find the support for the mood congruence theory, suggesting that game-induced mood has a positive and significant effect on the viewers’ Twitter sentiment towards the commercials. We also discuss both theoretical and practical implications for our study.
|11:10 - 11:35||Predicting Suicidal Ideation via Reddit Posts: a Text Mining Approach|
Uma Bhattacharyya, Yuheng Hu
Abstract: Suicide rates are at a 30 year high and suicide is now the 10th leading cause of death in the United States, according to the World Health Organization. People who suffer from mental health disorders are more likely to commit suicide because they feel out of place in society and struggle with internal problems. With the rise of social media, people have started to use social media as an outlet to talk about their lives and struggles. On websites like Reddit, special categories-- called 'subreddits'-- allow people with mental health disorders and people with suicidal thoughts to talk about their thoughts and feelings. Research has identified social media as an ideal environment for analyzing people’s behaviors. On social media sites, users feel more open to posting raw, emotional content than they would be at a psychiatrist’s office. This is because users typically post from a very familiar, natural environment, such as their homes, allowing them to be spontaneous and genuine with the content of their posts. This makes using social media content to detect mental health disorders such as depression very appealing. Recent research has examined whether the text in social media posts can detect mental health disorders. One study found that it is possible to predict depression in users based on the linguistic patterns in their social media posts. Through analysis of parts of speech used in social media posts, other researchers have found that it is possible to track a shift from having mental health disorder to having suicidal thoughts. In this research, we looked at posts on various subreddits related to mental health disorders and on the SuicideWatch subreddit which corresponds to suicidal ideation. We examined the words expressing different sentiments in users’ posts to see if it is possible to predict a user’s shift from having a mental health disorder-- posting on mental health related subreddits -- to having suicidal ideation-- posting on the SuicideWatch subreddit. Finding a way to predict a user’s shift in state of mind will be helpful for families to help their loved one’s who are struggling with mental health disorders and are thinking about suicide. Timely intervention can help to save many lives.
|11:35 - 12:00||Tactics and Tallies: A Study of the 2016 U.S. Presidential Campaign Using Twitter 'Likes'|
Yu Wang, Xiyang Zhang, Jiebo Luo
Abstract: We propose a framework to measure, evaluate, and rank campaign effectiveness in the ongoing 2016 U.S. presidential election. Using Twitter data collected from Sept. 2015 to Jan. 2016, we first uncover the tweeting tactics of the candidates and second, using negative binomial regression and exploiting the variations in ‘likes,’ we evaluate the effectiveness of these tactics.Thirdly, we rank the candidates’ campaign tactics by calculating the conditional expectation of their generated ‘likes.’ We show that while Ted Cruz and Marco Rubio put much weight on President Obama, this tactic is not being well received by their supporters. We demonstrate that Hillary Clinton’s tactic of linking herself to President Obama resonates well with her supporters but the same is not true for Bernie Sanders. In addition, we show that Donald Trump is a major topic for all the other candidates and that the women issue is equally emphasized in Sanders’ campaign as in Clinton’s. Finally, we suggest two ways that politicians can use the feedback mechanism in social media to improve their campaign: (1) use feedback from social media to improve campaign tactics within social media; (2) prototype policies and test the public response from the social media.
|12:00 - 12:25||Breakout Group 2Participants will self-select groups for the second breakout session based on the tractable entry points they find to be of interest in the first round. Groups will use this time to incubate research ideas, focusing on how might we take action on the outputs of the first round. At the end of the session, groups will report out on the projects they envision.|
|12:25 - 12:30||Closing remarks|