No abstract
Theories of privacy and how it relates to the use of Information Communication Technology (ICT) have been a topic of research for decades. However, little attention has been paid to the perception of privacy from the perspective of technology users in the Middle East. In this paper, we delve into interpretations of privacy from the approach of Arab Gulf citizens. We consider how privacy is practiced and understood in technology-mediated environments among this population, paying particular attention to the role of Islam and cultural traditions in constructing norms around privacy. We then offer culturally sensitive design principles and suggestions for future research that incorporates previously unexplored characteristics of privacy, which play a role in how users navigate social media.Comment: 12 pages, 1 figur
Social botnets have become an important phenomenon on social media. There are many ways in which social bots can disrupt or influence online discourse, such as, spam hashtags, scam twitter users, and astroturfing. In this paper we considered one specific social botnet in Twitter to understand how it grows over time, how the content of tweets by the social botnet differ from regular users in the same dataset, and lastly, how the social botnet may have influenced the relevant discussions. Our analysis is based on a qualitative coding for approximately 3000 tweets in Arabic and English from the Syrian social bot that was active for 35 weeks on Twitter before it was shutdown. We find that the growth, behavior and content of this particular botnet did not specifically align with common conceptions of botnets. Further we identify interesting aspects of the botnet that distinguish it from regular users.
The tragic Paris terrorist attacks of November 13, 2015 sparked a massive global discussion on Twitter and other social media, with millions of tweets in the first few hours after the attacks. Most of these tweets were condemning the attacks and showing support for Parisians. One of the trending debates related to the attacks concerned possible association between Muslims and terrorism, which resulted in a worldwide debate between those attacking and those defending Islam. In this paper, we use this incident as a case study to examine using online social network interactions prior to an event to predict what attitudes will be expressed in response to the event. Specifically, we focus on how a person's online content and network dynamics can be used to predict future attitudes and stance in the aftermath of a major event. In our study, we collected a set of 8.36 million tweets related to the Paris attacks within the 50 hours following the event, of which we identified over 900k tweets mentioning Islam and Muslims. We then quantitatively analyzed users' network interactions and historical tweets to predict their attitudes towards Islam and Muslims. We provide a description of the quantitative results based on the tweet content (hashtags) and network interactions (retweets, replies, and mentions). We analyze two types of data: (1) we use post-event tweets to learn users' stated stance towards Muslims based on sampling methods and crowd-sourced annotations; and (2) we employ pre-event interactions on Twitter to build a classifier to predict post-event stance. We found that pre-event network interactions can predict attitudes towards Muslims Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org. c 2017 ACM. ISBN 978-1-4503-2138-9.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.