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Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
 
Session Overview
Session
Paper Session 13: Information and Social Issues
Time:
Monday, 01/Nov/2021:
2:00pm - 3:30pm

Session Chair: John Budd, University of Missouri, USA
Location: Salon J, Lobby Level, Marriott

As time permits, moderators will facilitate reflective discussions at the end of sessions! These will be opportunities to have extra discussion on key points, synergies, and provocative elements of the papers.


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Presentations
2:00pm - 2:15pm
ID: 157 / PS-13: 1
Short Papers
Confirmation 1: I/we agree if this paper/presentation is accepted, all authors/panelists listed as “presenters” will present during the Annual Meeting and will pay and register at least for the day of the presentation.
Confirmation 2: I/we further agree presenting authors/panelists who have not registered on or before the early bird registration deadline will be removed from the conference program, and their paper will be removed from the Proceedings.
Confirmation 3: I/we acknowledge that all session authors/presenters have read and agree to the ASIS&T Annual Meeting Policies found at https://www.asist.org/am21/submission-types-instructions/
Topics: Archives; Data Curation; and Preservation
Keywords: 2021 Atlanta Spa Shootings, Anti-Asian Hate, Social Media Archive, Social Movement Archive, Twitter

#StopAsianHate: Archiving and Analyzing Twitter Discourse in the Wake of the 2021 Atlanta Spa Shootings

Lizhou Fan, Huizi Yu, Anne Gilliland

University of California, Los Angeles, USA

On March 16, 2021, six Asian women were killed in Atlanta, US, possibly out of racist motivations. This tragic event, now known as the 2021 Atlanta Spa Shootings, precipitated a massive increase in the volume of counter-anti-Asian declarations and discussion on social media platforms such as Twitter. In a pilot study to chronicle and profile public opinions, social movements and patterns in the global Twitter discourse we scraped the Twitter API using the query term “StopAsianHate”, obtaining more than 5.5 million tweets and their metadata. By using social movement analytical frameworks to analyze traffic peaks and the use of hashtags, we identified a set of more than 300 frequently used hashtags that can serve as specific query words in future archival ingest activities, as well as the dimensions of and current problems with this social movement. This suggests the utility of this approach for both archiving applications and social-political analyses of emerging topics and concerns.



2:15pm - 2:30pm
ID: 121 / PS-13: 2
Short Papers
Confirmation 1: I/we agree if this paper/presentation is accepted, all authors/panelists listed as “presenters” will present during the Annual Meeting and will pay and register at least for the day of the presentation.
Confirmation 2: I/we further agree presenting authors/panelists who have not registered on or before the early bird registration deadline will be removed from the conference program, and their paper will be removed from the Proceedings.
Confirmation 3: I/we acknowledge that all session authors/presenters have read and agree to the ASIS&T Annual Meeting Policies found at https://www.asist.org/am21/submission-types-instructions/
Topics: Social Media and Social Computing
Keywords: Content moderation, hate speech classification, political discussion, YouTube comment, political orientation

Content Moderation of Speech in Political Discussions

Yisi Sang, Lizhen Liang, Jeffrey Stanton

Syracuse University, USA

Social media platforms have been hailed as “politically disruptive communication technologies'' (Hong & Nadler, 2012). Individuals express opinions and engage with politicians, the press, and each other on social media, sometimes using offensive language (Rossini et al., 2020). Content moderation has been adopted by many social media platforms to screen and evaluate offensive speech. In the present study we trained offensive speech classifiers to analyze offensive speech examples by integrating three archival datasets. We then used the trained classifier to examine a large body of comments about YouTube videos posted during the 2018 midterm election cycle. This provided information on the prevalence of various kinds of offensive comments and the pattern of content moderation used by YouTube. We also examined comment negativity using data from offensive speech lexicons. Our results showed systematic variance in the prevalence of speech topics depending upon the political orientation of the content. Language use was significantly different between left and right-leaning videos for comments related to sexism.



2:30pm - 3:00pm
ID: 287 / PS-13: 3
Long Papers
Confirmation 1: I/we agree if this paper/presentation is accepted, all authors/panelists listed as “presenters” will present during the Annual Meeting and will pay and register at least for the day of the presentation.
Confirmation 2: I/we further agree presenting authors/panelists who have not registered on or before the early bird registration deadline will be removed from the conference program, and their paper will be removed from the Proceedings.
Confirmation 3: I/we acknowledge that all session authors/presenters have read and agree to the ASIS&T Annual Meeting Policies found at https://www.asist.org/am21/submission-types-instructions/
Topics: Social Media and Social Computing
Keywords: Chinesevirus; Twitter; COVID-19; xenophobia; racist hashtag

Racist Framing through Stigmatized Naming: A Topical and Geo-locational Analysis of #Chinavirus and #Chinesevirus on Twitter

Miyoung Chong1, Haihua Chen2

1University of Virginia, USA; 2University of North Texas, USA

During the COVID-19 pandemic, racists remarks accompanied by racist hashtags were disseminated via social media. Particularly, Asian Americans in the U.S. have been suffered from racism and xenophobia resulting in physical violence and mental harassment in many cases. Despite the major function of the social media as an open access platform for unedited and free speech for people with diverse background, the global episodes of the soaring racism and xenophobia occurred in online public arenas reaffirmed that the platforms could be used for a nurturing ground of racism and xenophobia. This study examined the top influencers in the racist hashtag Twitter network and top shared neighboring hashtags with #Chinavirus or #Chinesevirus. We extracted topics from the racist hashtag Twitter network applying the state-of-the-art BERTopic modeling technique and conducted a geo-locational analysis of the participants of the network globally and by U.S. states. Trump was identified as the most influential actor in the #Chinavirus and #Chinesevirus Twitter network. This study confirmed previous literature that political elite’s public communication strategy to deviate the attention of the public suffered from the new disease and went through hardships under the epidemic crisis.



3:00pm - 3:15pm
ID: 243 / PS-13: 4
Short Papers
Confirmation 1: I/we agree if this paper/presentation is accepted, all authors/panelists listed as “presenters” will present during the Annual Meeting and will pay and register at least for the day of the presentation.
Confirmation 2: I/we further agree presenting authors/panelists who have not registered on or before the early bird registration deadline will be removed from the conference program, and their paper will be removed from the Proceedings.
Confirmation 3: I/we acknowledge that all session authors/presenters have read and agree to the ASIS&T Annual Meeting Policies found at https://www.asist.org/am21/submission-types-instructions/
Topics: Technology; Culture; and Society
Keywords: Body-worn cameras, surveillance, policing, information politics, visual evidence

Interpreting Police Video: A Pilot Study

Bryce Newell

University of Oregon, USA

The visual records police body-worn cameras (BWCs) produce are frequently characterized as presenting more complete, comprehensive, and objective evidence of police-public encounters than other forms of evidence. Despite a growing body of research on the social impacts of BWCs, we still lack a rich understanding of what information these technologies provide viewers. This ongoing exploratory project examines how people interpret what they see in BWC footage and what judgments they make about the appropriateness of depicted police conduct. Drawing from interviews with twelve students and twelve sworn police officers, I present initial exploratory findings. Participants viewed BWC video of a police-public contact in which an officer stops a man on a sidewalk to question him, resulting in a foot chase and, ultimately, an arrest. When asked whether the officer’s behavior was justified, police officer participants were more likely to focus on things like police training, procedure, and legality to justify the officer’s action, while student participants were more likely to focus on the officer’s demeanor, reporting that he should have been calmer and may have escalated the situation by not explaining clearly why he had initiated the stop.



 
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