Preliminary Conference Agenda

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Session Overview
Session
Completed Papers 12: Social Media 2
Time:
Wednesday, 28/Mar/2018:
9:00am - 10:30am

Session Chair: Andrew Martin Cox, University of Sheffield
Location: Lecture Theatre 2 (Diamond)
The Diamond

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Presentations

Understanding Interactions Between Municipal Police Departments and the Public on Twitter

Yun Huang, Qunfang Wu

Syracuse University, United States of America

Law enforcement agencies have started using social media for building community policing, i.e., establishing collaborations between the people in a community and local police departments. Both researchers and practitioners need to understand how the two parties interact on social media on a daily basis, such that effective strategies or tools can be developed for the agencies to better leverage the platforms to fulfill their missions. In this paper, we collected 9,837 tweets from 16 municipal police department official Twitter accounts within 6 months in 2015 and annotated them into different strategies and topics. We further examined the association between tweet features (e.g., hashtags, mentions, content) and user interactions (favorites and retweets) by using regression models. The models reveal surprising findings, e.g., that the number of mentions has a negative correlation with favorites. Our findings provide insights into how to improve interactions between the two parties.


Automated Diffusion? Bots and their Influence During the 2016 U.S. Presidential Election

Olga Boichak, Sam Jackson, Jeff Hemsley, Sikana Tanupabrungsun

Syracuse University, United States of America

In the 2016 U.S. Presidential election, some candidates used to automated accounts, or bots, to boost their social media presence and followership. Categorizing all automated accounts as “bots” obfuscates the role different types of bots play in the spread of political information in election campaigns. Exploring strategies for automated information diffusion helps scholars understand and model online political behavior. This paper presents an initial effort aimed at understanding the disparate roles of bots in diffusion of political messages on Twitter. Having collected over 300 million tweets from candidates and the public from the U.S. presidential election, we use three OLS regression models to explore the strategic advantages of different types of automated accounts. We approach this by analyzing retweet events, testing a series of hypotheses regarding bots’ influence on the size of retweet events, and the change in candidates’ followers. Next, we develop an estimator to analyze the spread of information across the networks, demonstrating that, while ‘benevolent bots’ serve as overt information aggregators and have an effect on information diffusion, “nefarious bots” act as false amplifiers, covertly mimicking the spread of online information with no effect on diffusion. Making this important distinction allows us to disambiguate the concept of “bots” and reach a more nuanced and detailed understanding of the role of automated accounts in information diffusion in political campaigning online.


Auto-Tracking Social Discussions on Corporate Facebook Page: A Case Study on Starbucks

Bei Yu, Yihan Yu

Syracuse University, United States of America

This study proposed and validated a topic modeling-based approach for auto-tracking customer dialog on social media, using Starbucks as a case study because of its pioneering social media practice in service industry. A topic model was fit based on nearly 150,000 customer comments posted to Starbucks' Facebook page in 2013. This model was able to identify not only business-related topics, such as customer responses to marketing campaigns, but also controversial topics regarding community involvement and corporate social responsibility, such as gay, gun, and government. Guided by this topic model, each topic's evolving dynamics and patterns of user participation were further revealed, providing a bird's-eye view of the topics and their evolution. The case study has demonstrated that the proposed approach can effectively track the main themes in the customer dialog on social media, zoom in on the controversial topics, measure their time spans, and locate the participants and the vocal activists. Such information would be valuable input for companies to design their intervention strategies and evaluate the outcomes in social media discussions.



 
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