Social Media: Culture, Identity, and Inclusion Minitrack
Permanent URI for this collection
Digital and social media have become ubiquitous, seemingly essential pieces of infrastructure necessary for realizing a globalized society. Social media brings diverse groups of people together in productive and, at times, puzzling ways. Users from different cultures, genders, and generations have adopted social media in interesting manners, creating new norms and practices. As scholars of systems sciences, it is important for us to examine not only these new and various communication and behavioral styles but also to explore the design, impact and influence digital and social media have within these groups of users.
Potential Topics:
- Inter-cultural and Cross-cultural use of Social Media
- Gendered Social Media
- Digital Natives/ Digital Immigrants: does the difference still exist?
- The construction and circulation of gender, sexuality, race, ethnicity, religion and disability through the use of social media
- Online Harassment
- Identity Enactment and Policing
- Digital/Immaterial Labour
- Reproduction of biases
- Governance and Rules in Action
- Aging and Social Media
- Inter-generational use of Social Media
- Impact and influence of social media on diversity
- Social Media, MOOCS, & Inclusion
- Social Media & Implicit Bias
- Social Media, Culture & Change/Social Innovation
- Social Media & Intersectionality
- Designing Social Media for Inclusion
- Social Media, Disruptive Innovation, and Capacity-Building For All
This mini track is also open to receiving papers on additional, relevant topics and using in innovative ways social media itself.
Minitrack Co-Chairs:
Shahper Vodanovich (Primary Contact)
Auckland University of Technology, New Zealand
Email: svodanov@aut.ac.nz
Nanette S. Levinson
American University
Email: nlevins@american.edu
Amanda Menking
University of Washington, Seattle
Email: amenking@uw.edu
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Recent Submissions
1 - 7 of 7
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ItemUsing the Control Balance Theory to Explain Social Media Deviance( 2017-01-04)Online Social Media Deviance (OSMD) is one the rise; however, research in this area traditionally has lacked a strong theoretical foundation. Following calls to reveal the theoretical underpinnings of this complex phenomenon, our study examines the causes of OSMD from several novel angles not used in the literature before, including: (1) the influence of control imbalances (CIs) on deviant behavior, (2) the role of perceived accountability and deindividuation in engendering CI, (3) and the role of IT in influencing accountability and deindividuation. Using an innovative factorial survey method that enabled us to manipulate the IT artifacts for a nuanced view, we tested our model with 507 adults and found strong support for our model. The results should thus have a strong impetus not only on future SM research but also for social media (SM) designers who can use these ideas to further develop SM networks that are safe, supportive, responsible, and constructive.
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ItemSocial Media Usage and Cultural Dimensions: an Empirical Investigation( 2017-01-04)Cultural attributes of employees affect organizations in several different ways through their impact on organizational goals and decision-making processes. Social media create ample opportunities for organizations to improve competitiveness and efficiency of marketing and communications. We empirically investigate the impact of employee cultural dimensions on social media usage at work and at home. Such a study has not been undertaken before to the best of our knowledge and this would be the first study to connect cultural dimension characteristics of individuals with social media usage. Specifically, we investigate the effect of Power Distance (PD), Uncertainty Avoidance (UA), and Individualism-Collectivism (IC) on the use of popular social media platforms such as Facebook, Twitter, Skype, and LinkedIn. Our results show that certain cultural dimensions predict higher or lower levels of use of specific social media platforms. We provide implications of our results on research and practice.
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ItemMapping Articles on China in Wikipedia: An Inter-Language Semantic Network Analysis( 2017-01-04)This article describes an inter-language semantic network analysis examining the differences between articles about China in the Chinese and English versions of Wikipedia. It explores the differences in the content of Wikipedia through (a) correlation analysis of semantic networks and (b) the salience of semantic concepts through their network centralities. The results suggest there is high dissimilarity between the semantic content of the English and Chinese versions of articles on China. While both pages focused on government, population, language, character, diplomatic relations, development of the economy, and science and technology, the Chinese-speaking and English-speaking contributors framed the article on China differently—according to dissimilarities in cultures, values, interests, situations, and emotions of different language groups. This research contributes to the literature and understanding of how culture of different language groups influences the process of crowdsourcing knowledge on online collaboration platforms.
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ItemGender (In)Consistent Communication via Social Media and Hireability: An Exploratory Study( 2017-01-04)Using social media in employee selection processes is relatively new behavior that raises many important questions. Although managers report using sites like Facebook to review applicants, little is known about how these sites influence assessments of those candidates. This exploratory study reports on an experiment designed to evaluate competing hypotheses regarding (in)consistent gender norm-based communication on Facebook and subsequent attraction and hiring decisions. All participants were required to have been responsible for actual hiring decisions during their careers. Surprisingly, results are in contrast to research on the selection process and show that feminine-style communication on Facebook is perceived as most attractive and hirable. However, masculine communication is perceived as least attractive and hirable. This effect was consistent regardless of applicant gender. Practical implications, strengths, limitations, and directions for future research are discussed.
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ItemDetecting Offensive Statements towards Foreigners in Social Media( 2017-01-04)Recently, politicians and media companies identified an increasing number of offensive statements directed against foreigners and refugees in Europe. In Germany, for example, the political group “Pegida” drew international attention by frequently publishing offensive content concerning the religion of Islam. As a consequence, the German government and the social network Facebook cooperate to address this problem by creating a task force to manually detect offensive statements towards refugees and foreigners. In this work, we propose an approach to automatically detect such statements aiding personnel in this labor-intensive task. In contrast to existing work, we assess severity values to offensive statements and identify the referenced targets. This way, we are able to selectively detect hostility towards foreigners. To evaluate our approach, we develop a dataset containing offensive statements including their target. As a result, a substantial amount of offensive statements and a moderate amount of the referenced victims was detected correctly.
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ItemChallenges in Modifying Existing Scales for Detecting Harassment in Individual Tweets( 2017-01-04)In an effort to create new sociotechnical tools to combat online harassment, we developed a scale to detect and measure verbal violence within individual tweets. Unfortunately, we found that the scale, based on scales effective at detecting harassment offline, was unreliable for tweets. Here, we begin with information about the development and validation of our scale, then discuss the scale’s shortcomings for detecting harassment in tweets, and explore what we can learn from this scale’s failures. We explore how rarity, context, and individual coder’s differences create challenges for detecting verbal violence in individual tweets. We also examine differences in on- and offline harassment that limit the utility of existing harassment measures for online contexts. We close with a discussion of potential avenues for future work in automated harassment detection.
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