MIS Distinguished Speaker Series

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October 12 – Alan Dennis to Present “Fake News on Social Media”

By

Fake News on Social Media

by

Alan R. Dennis

John T. Chambers Chair of Internet Systems

Kelley School of Business, Indiana University

Friday, October 12, 2018

10:30 AM – noon

Speakman Hall Suite 200

 

Abstract

Fake news on social media has received much media attention and many experts believe it influenced the 2016 US Presidential election and the 2016 Brexit vote. More than 60% of Americans consume news on social media, and 84% believe they can detect fake news. But can they? We studied the ability of experienced social media users to detect fake news, and how seeing news headlines – both real and fake – influenced their cognition. Only 18% of subjects could detect fake news better than chance; 82% of users could have made better judgments by flipping a coin. We found that confirmation bias dominates, with users essentially unable to distinguish real news from fake news, and that cognition is driven by how well a news headline aligns with the user’s prior political beliefs.

We conducted a series of studies examining different ways in which the social media user interface could be designed, including how news headlines are presented, and the effects of quality ratings. These different interface designs had different effects on the extent to which users believed social media stories, and how likely they were to read, like, comment on and share the stories.

Feb 8: Shawndra Hill to present on Talkographics: Using What Viewers Say Online to Calculate Audience Affinity Networks for Social TV-based Recommendations

By

Shawndra Hill
Assistant Professor, OIM Dept
The Wharton School, University of Pennsylvania

February 8, 2013
Speakman Hall 200, 1000am – 1130am
Seminar Title : Talkographics: Using What Viewers Say Online to Calculate Audience Affinity Networks for Social TV-based Recommendations

Abstract
Viewers of TV shows are increasingly taking to online sites like Facebook and Twitter to comment about the shows they watch as well as to contribute content about their daily lives. We present a novel recommendation system (RS) based on the user-generated content (UGC) contributed by TV viewers via the social networking site Twitter. In our approach, a TV show is represented by all of the tweets of its viewers who follow the show on Twitter. These tweets, in aggregate, enable us to reliably calculate the affinity between TV shows and to describe how and why certain shows are similar in terms of their audiences in a privacy friendly way. This paper’s two main contributions are: 1) a new methodology for collecting data from social media — including information about product networks (or how shows are connected through users on a social network), geographic location, and user-contributed text comments — which can be used to generate affinity networks and test them; and 2) a new privacy friendly UGC-based RS that relies on all publicly-available text contributed by viewers, as opposed to only pre-selected keywords extracted from the UGC associated with the shows, a specific ontology or taxonomy, which makes our approach more flexible and generalizable than those used in any prior research. We show that our approach predicts remarkably well the TV shows that Twitter users follow. We also explain why the approach works so well: First, we show that the UGC reflects the demographics, geographic location, and psychographics of viewers, and coin the term talkographics to refer to descriptions of a TV show’s viewers — or in general any product’s audience — that are revealed by the words used in text messages sent by Twitter-using TV viewers; second, we show that Twitter text can represent many complex nuanced combinations of the demographic, geographic, and psychographic features of the audience; third, we show that we can use talkographic profiles to first calculate similarities between TV shows, then use these similarities reliably in RSs; we also show that our approach can be combined with a product association network approach to achieve even better recommendations; finally, we show that our text-based approach performs best for shows for which there is a demographic bias to the viewing audience compared to those that do not have a demographic bias. To demonstrate that our RS is generalizable, we apply the same approach to followers of clothing and automobile retailers.

 

Oct 19: Paul Leonardi to speak on Using Social Technologies to Learn “Who Knows What” and “Who Knows Whom” in the Organization

By

Paul Leonardi
Pentair-Nugent Associate Professor
Kellogg School of Management, Northwestern University

October 19, 2012
Speakman Hall 200, 1000am – 1130am
Seminar Title :  Using Social Technologies to Learn “Who Knows What” and “Who Knows Whom” in the Organization

Abstract
In most discussions of intra-organizational knowledge sharing, the words “search” and “transfer” are never far apart. Organizational theorists normally presume that before knowledge can be transferred, someone has to find where that knowledge resides through an active search process. I propose an alternative antecedent to knowledge transfer than search by developing the concept of ambient awareness. Through routine exposure to ambient communication – communications happening around us that we don’t partake in, but that we can eavesdrop upon – we begin to learn who knows what and who knows whom. By developing ambient awareness, we are ready, when the time comes to request a knowledge transfer to simply ask the right person for it or for access to it – we don’t first have to engage in lengthy procedures to search for it, nor do we have to maintain dual networks that support search and transfer simultaneously. I propose that the use of social media tools within organizations can overcome problems associated with the development of ambient awareness because they make messages transparent and networks visible. Through our exposure to these transparent messages and visible networks we develop an awareness of who knows whom and who knows what.

© Temple Community Platform, Patent Pending.

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