Characterizing silent users in social media communities

Silent users often constitute a significant proportion of an online user-generated content system. In the context of social media such as Twitter, users can opt to be silent all or most of the time. They are often called the invisible participants or lurkers. As lurkers contribute little to the online content, existing analysis often overlooks their presence and voices. However, we argue that understanding lurkers is important in many applications such as recommender systems, targeted advertising, and social sensing. This research therefore seeks to characterize lurkers in social media and propose methods to profile them. We examine 18 weeks of tweets generated by two Twitter communities consisting of more than 110K and 114K users respectively. We find that there are many lurkers in the two communities, and the proportion of lurkers in each community changes with time.We also show that by leveraging lurkers' neighbor content, we are able to profile them with accuracy comparable to that of profiling active users. It suggests that user generated content can be utilized for profiling lurkers and lurkers in Twitter are after all not that ``invisible''

Prediction of U.S. Election Using Twitter Data: A Case Study

Social network/media has become popular over the last few years and is moving closer to be an integral part in one�s life. With the rise of new social media movement, the analysis of social networking blog contents has become an important tool of big data analytics. Recent research studies on the use of Twitter for predicting political elections have raised many questions as well as interest in using Twitter data for predictive analysis. The overarching objective of our research is to study the capability of Twitter data as an ex-ante indicator of event outcomes. The 2014 US midterm election has been chosen as the event for this study. This work analyses both pre-poll and post-poll data from Twitter related to 2014 midterm elections in U.S. Relevant tweets are extracted from the tweet stream with the help of a Map-Reduce Program in a Hadoop system by specifying appropriate keywords configuration for running Apache Flume. This data are classified into four groups using �Democrat� and �Republican� as the division criteria. Two time-series of sentiments (positive and negative) are constructed for each group. Several statistics are also compiled from each group of tweets and used as predictive indicators. Original tweet count, retweet count, and user count in each group are some of the statistics compiled. All the statistics favor the Republican party to win which actually was the outcome of the election. Our research consists of two parts. The first part is prediction of election results and the second part is modeling sentiment before and after the election. We used Hidden Markov Model as a tool for both parts. The hidden states of the model were used as sentiment indicators and state changes were interpreted as sentiment changes. The results of the HMM agreed with the actual outcomes. Our study provides support for the argument that Twitter data can be considered as a reliable predictor of events.Computer Scienc

Social informatics

5th International Conference, SocInfo 2013, Kyoto, Japan, November 25-27, 2013, Proceedings</p

CLINICAL AND SOCIAL PATHWAYS TO CARE: A COMPUTATIONAL EXAMINATION OF SOCIAL MEDIA FOR MENTAL HEALTH CARE

In the last decade, powered by connectivity to large social networks and advances in collecting and analyzing digital traces of individuals from social media platforms, researchers have gleaned rich insights into individuals’ and populations’ mental health states and experiences, including their moods, emotions, social interactions, language, and communication patterns. Using these inferences, researchers have been able to study support-seeking behaviors, distinguishing patterns, risk markers, and diagnosis states for mental illnesses from social media data, promising a fundamental change in mental health care. What we need next in this line of work is for data and algorithms based on social media to be contextualized in people’s pathways to mental health care. However, there are several challenges and unanswered questions that present hurdles. First, gaps exist in the psychometric validity of social media based measurements of behaviors and the utility of these inferences in predicting clinical outcomes in patient populations. Second, if social media can act as an intervention platform, outside of discrete events, a holistic understanding of its role in people’s lives along the course of a mental illness is crucial. Lastly, several questions remain around the ethical implications of research practices in engaging with a vulnerable population subject to this research. This thesis charts out empirical and critical understandings and develops novel computational techniques to ethically and holistically examine how social media can be employed to support mental health care. Focusing on schizophrenia, one of the most debilitating and stigmatizing of mental illnesses, this thesis contributes a deeper understanding on pathways to care via social media along three themes: 1) prediction of clinical mental health states from social media data to support clinical interventions, 2) understanding online self-disclosure and social support as pathways to social care, and 3) the intersection of social and clinical pathways to care along the course of mental illness. In doing so, this work combines theories from social psychology, computer-mediated communication, and clinical literature with machine learning, statistical modeling, and natural language analysis methods applied on large-scale behavioral data from social media platforms. Together, this work contributes novel methodologies and human-centered algorithmic design frameworks to understand the efficacy of social media as a mental health intervention platform, informing clinicians, researchers, and designers who engage in developing and deploying interventions for mental health and well-being.Ph.D