Preliminary Conference Agenda

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This agenda is preliminary and subject to change.

 
Session Overview
Session
Papers 13: Social-Media Text Mining and Sentiment Analysis
Time:
Tuesday, 02/Apr/2019:
10:30am - 12:00pm

Session Chair: Peter Organisciak, University of Denver
Location: 2110/2111/2112

Presentations

Impact of Reddit Discussions on Use or Abandonment of Wearables

R. Garg, J. Kim

Syracuse University, United States of America

Discussion platform, Reddit, is the third most visited website in the US. People can post their questions on this platform to get varying opinions from fellow users, which in turn might also influence their behavior and choices. Wearables are becoming widely adopted, yet challenges persist in their effective long term use because of technical and device related, or personal issues. Therefore, by employing sentiment analysis, this paper aims to analyze how decisions of use or abandonment of wearables are influenced by discussions on Reddit. The results are based on the analysis of 6680 posts and their associated 50,867 comments posted between December 2015 - December 2017 on the subreddit (user created groups) on android wear. Our results show that sentiment of the discussion is majorly dictated by the sentiment of the post itself, and people decide to continue using their devices when fellow Redditors offer them workarounds, or the discussion receives majority of positive or fact-driven neutral comments.



Spatiotemporal Analysis on Sentiments and Retweet Patterns of Tweets for Disasters

S. Chen, J. Mao, G. Li

Wuhan University, China, People's Republic of

Twitter provides an important channel for public to share feelings, attitudes and concerns about disasters. In this study, we aim to explore how spatiotemporal factors affect people's sentiment in disaster situations and how the area type, time stage and sentiment of the tweets affect the extent and speed of tweets' diffusion. After analyzing 531,912 geo-tagged tweets about Hurricane Harvey, we found that on-site tweets are more positive than off-site tweets across the time; neutral tweets spread broader and faster than tweets with sentiment propensity; on-site tweets and tweets posted at early stages tend to be more popular. These findings could enable authorities and response organizations to better comprehend people's feelings and behaviors in social media and their changes over time and space. In future, we will analyze the influence of the interactions among sentiment, location and time to retweet behaviors.



Analyzing sentiment and themes in fitness influencers’ Twitter dialogue

B. E. Auxier, C. Buntain, J. Golbeck

University of Maryland College Park, United States of America

Social media allows anyone to distribute content and build an audience. Natural language processing, sentiment analysis, and psycholinguistic text analysis have proven to be powerful tools for characterizing and classifying social media text. Furthermore, the combination text and sentiment analysis have allowed researchers to identify influencers both by their structural roles and the content they produce. In this paper, we investigate fitness-oriented social media influencers. This research set out to understand how fitness influencers (N=92) on Twitter speak to their audiences through the-matic and sentiment analysis of their tweets (N=273,868). Findings suggest sentiment and topics discussed vary between male and female health and fitness influencers on the platform. The analysis also determined no senti-ment differences between self-identified fitness trainers/coaches and influ-encers who do not identify as such. The results have implications for per-sonalization and recommendation algorithms that operate in this space.



Political Popularity Analysis in Social Media

A. Karami, A. Elkouri

University of South Carolina, United States of America

Popularity is a critical success factor for a politician and her/his party to win in elections and implement their plans. Finding the reasons behind the popularity can provide a stable political movement. This research attempts to measure popularity in Twitter using a mixed method. In recent years, Twitter data has provided an excellent opportunity for exploring public opinions by analyzing a large number of tweets. This study has collected and examined 4.5 million tweets related to a US politician, Senator Bernie Sanders. This study investigated eight economic reasons behind the senator's popularity in Twitter. This research has benefits for politicians, informatics experts, and policymakers to explore public opinion. The collected data will also be available for further investigation.