Loading…
2017 #SMSociety Theme: Social Media for Social Good or Evil

Our online behaviour is far from virtual–it extends our offline lives. Much social media research has identified the positive opportunities of using social media; for example, how people use social media to form support groups online, participate in political uprising, raise money for charities, extend teaching and learning outside the classroom, etc. However, mirroring offline experiences, we have also seen social media being used to spread propaganda and misinformation, recruit terrorists, live stream criminal activities, reinforce echo chambers by politicians, and perpetuate hate and oppression (such as racist, sexist, homophobic, and anti-Semitic behaviour).

Sign up or log in to bookmark your favorites and sync them to your phone or calendar.

Session 6B [clear filter]
Sunday, July 30
 

15:30

Session 6B: Bots
Moderators
avatar for Lina Gomez

Lina Gomez

Assistant Professor, Universidad del Este
Assistant Professor, Communication Program, School of Social Sciences and Humanities, Universidad del Este, Puerto Rico. PhD in Business with a concentration in Corporate Social Responsibility and Organizational Sustainability from Universitat Jaume I in Castellón, Spain. Research... Read More →

Sunday July 30, 2017 15:30 - 17:00
TRS 1-075 - 7th Flr Ted Rogers School of Management, Ryerson University 55 Dundas Street West, Toronto, ON M5G 2C9

15:31

Can We Trust Social Media Data? Social Network Manipulation By An IoT Botnet [FULL]
Authors: Masarah Paquet-Clouston, Olivier Bilodeau and David Décary-Hétu

Abstract: The size of an account’s audience – in terms of followers or friends count – is believed to be a good measure of its influence and popularity. To gain quick artificial popularity on online social networks (OSN), one can buy likes, follows and views from social media fraud (SMF) services. Social media fraud (SMF) is the generation of likes, follows and views on OSN such as Facebook, Twitter, YouTube and Instagram. Using a research method that combines computer sciences and social sciences, this paper provides a deeper understanding of the illicit market for SMF. It conducts a market price analysis for SMF, describes the operations of a supplier – an Internet of things (IoT) botnet performing SMF – and provides a profile of the potential customers of such fraud. The paper explains how an IoT botnet conduct social network manipulation and illustrates that the fraud is driven by OSN users, the entertainers, small online shops and private users that create the demand. It also illustrates that OSN strategy to suspend fake accounts only cleans the networks a posteriori of the fraud and does not deter the crime – the botnet – or the fraud – SMF – from happening. Several solutions to deter the fraud are provided.

Sunday July 30, 2017 15:31 - 17:00
TRS 1-075 - 7th Flr Ted Rogers School of Management, Ryerson University 55 Dundas Street West, Toronto, ON M5G 2C9

15:31

Identifying Bots In The Australian Twittersphere [WIP]
Author: Brenda Moon

Abstract: Identification of bots on Twitter can be difficult, and successful approaches often use an iterative workflow, applying different techniques to identify different groups of bots. This paper presents first results of the application of this iterative workflow to the Australian TrISMA collection, which contains the tweets of over 4 million Twitter accounts identified as being Australian. To our knowledge this research undertakes the first comprehensive identification of bots in the Australian Twittersphere. The identified bots are then classified by bot type and the proportion of overall account and tweet numbers they represent determined.

Sunday July 30, 2017 15:31 - 17:00
TRS 1-075 - 7th Flr Ted Rogers School of Management, Ryerson University 55 Dundas Street West, Toronto, ON M5G 2C9

15:31

Twitter Bot Surveys: A Discrete Choice Experiment To Increase Response Rates [WIP]
Authors: Juan Pablo Alperin, Erik Warren Hanson, Kenneth Shores and Stefanie Haustein

Abstract: This paper presents a new methodology—the Twitter bot survey—that bridges the gap between social media research and web surveys. The methodology uses the Twitter APIs to identify a target population and then uses the API to deliver a question in the form of a regular Tweet. We hypothesized that this method would yield high response rates because users are posed a question within the social media platform and are not asked, as is the case with most web surveys, to follow a link away to a third party. To evaluate the response rate and identify the most effective mechanism for increasing it, we conducted a discrete choice experiment that evaluated three factors: question type, the use of an egoistic appeal, and the presence of contextual information. We found that, similar to traditional web surveys, multiple choice questions, egoistic appeals, and contextual information all contributed to higher response rates. Question variants that combined all three yielded a 40.0% response rate, thereby outperforming most other web surveys and demonstrating the promise of this new methodology. The approach can be extended to any other social media platforms where users typically interact with one another. The approach also offers the opportunity to bring together the advantages of social media research using APIs with the richness of information that can be collected from surveys.

Sunday July 30, 2017 15:31 - 17:00
TRS 1-075 - 7th Flr Ted Rogers School of Management, Ryerson University 55 Dundas Street West, Toronto, ON M5G 2C9