Monday, 3:30pm


#Criming and #Alive: Network and Content Analysis of Two Sides of a Story on Twitter

Vanessa Lynn Kitzie, Debanjan Ghosh
Rutgers University, United States of America


On December 3, 2014, after a grand jury decided not to indict the white police officer in the death of Eric Garner, the social networking platform Twitter was flooded with tweets sharing stances on racial profiling and police brutality. To examine how issues concerning race were communicated and exchanged during this time, this study compares differences between tweets using two trending hashtags #CrimingWhileWhite (#cww) and #AliveWhileBlack (#awb) from December 3 through December 11, 2014. To this end, network and content analysis are used on a large dataset of tweets containing the hashtags #awb and #cww. Findings indicate that there are clear differences, both structurally and in linguistic style, between how individuals express themselves based on which hashtag they used. Specifically, we found that #cww users disproportionately shared informational content, which may have led to the hashtag gaining more network volume and attention as a trending topic than #awb. In contrast, #awb tweets tended to be more subjective, expressing a sense of community and strong negative sentiment toward persistent structural racism.

Analyzing MOOC Discussion Forum Messages to Identify Cognitive Learning Information Exchanges

Jian-Syuan Wong, Bart Pursel, Anna Divinsky, Bernard {Jim} Jansen
Pennsylvania State University, United States of America


While discussion forums in online course have been studied in the past, no one has proposed a model linking messages in discussion forums to a learning taxonomy, even though forums are widely used as educational tools in online courses. In this research, we view forums as information seeking events and use a keyword taxonomy approach to analyze a large amount of MOOC forum data to identify the types of learning interactions taking place in forum conversations. Using 51,761 forum messages from 8,169 forum threads from a MOOC with a 50,000+ enrollment, messages are analyzed based on levels of Bloom’s Taxonomy to categorize the scholarly discourse. The results of this research show that interactions within MOOC discussion forums are a learning process with unique characteristics specific to particular cognitive learning levels. Results also imply that different types of forum interactions have characteristics relevant to particular learning levels, and the volume of higher levels of cognitive learning incidents increase as the course progresses.

Building a Parsimonious Model for Identifying Best Answers Using Interaction History in Community Q&A

Chirag Shah
Rutgers University, United States of America


Evaluating answer quality or identifying/predicting which answer would be selected as the best for a given question is an important problem in community-based Q&A services. In this article we introduce new interaction-based features depicting the amount of distinct interactions between an asker and answerer over time, in order to predict whether an answer will be selected as Best Answer or not within Yahoo! Answers. Through a series of experiments ran on a data set of 23,218 question-answer pairs, we determined that after the data was first run using a model trained on textual features, and then the failed cases re-run with a model trained on interaction features, we were able to significantly improve the performance of the original model in identifying these difficult cases. In addition, when compared to models using often five to seven times the amount of features and requiring a large amount of computational effort, our model performed at to above the same evaluative measures. This suggests that future classification models can be made more parsimonious and handle larger datasets using less computational effort by developing a two-step classifier that includes interaction history as a feature.

Social Search Behavior in a Social Q&A Service: Goals, Strategies, and Outcomes

Grace YoungJoo Jeon, Soo Young Rieh
University of Michigan, United States of America

Recent advancements in social technologies allow information seekers to reach out to a larger, more distributed group of people online when searching for information. In this study, people’s question-asking behavior using a social Q&A service is conceptualized as social search behavior. We are particularly interested in investigating social search goals, strategies, tactics, informational outcomes, and social outcomes. We collected a total of 406 questions posted on Yahoo! Answers by 78 participants over one week. Interviews based on those questions and answers they received were conducted and content-analyzed. We identify five distinct search strategies and 15 tactics positioned on a continuum of two different dimensions in terms of answer quantity and answer quality. Pursuit of quantity or quality is influenced by five categories of goals identified in this study. The goals and associated strategies and tactics also influence people’s perceived informational outcomes and social outcomes. Contributions of this study to the social search research community and implications for practitioners in the area of social Q&A services are discussed.