The interest of this research lies in the controversial online teen #FreeJahar movement calling for the freedom of the Boston bombing suspect Dzhokhar Tsarnaev because teenage girls believe he is “too beautiful to be a terrorist” (Nelson, 2013). Concerns were raised that in on-line forums the younger Boston Bombing suspect appeared to attract a cult teen following expressing affection and concern for him (DailyMail, 2013). Facebook, Tumblr tribute accounts were set up in support of the teen with the #FreeJahar Twitter tag, which were trending when these activities first appeared. Teen activities in Twitter do need to be monitored (Wiederhold, 2012) as observed below:
- “I can’t be the only one who finds the suspected bomber to be sexy, can I?” 19 April 2013.
- “i don’t even care if jahar is a terrorist he’s cute i don’t want him to die.” @******, 20 April 2013.
- “I’m not gonna lie, the second bombing suspect, Dzhokhar Tsarnaev, is hot. #sorrynotsorry” @******, 20 April 2013.
- “Yes I like Justin Bieber and I like Jahar but that has nothing to do with why i support him. I know hes innocent, he is far too beautiful” @******, 25 April 2013.
- “How many RT’s for our boy jahar look at that beautiful face #freejahar pic.twitter.com/K9xKFvv5HT” @******, 4 May 2013.
- “Getting one of Jahar’s tweets tattooed on me tomorrow. Guess you could say I’m a #FreeJahar supporter,” @******, 7 May 2013.
I started building scalable algorithms on scalable hardware-software platforms which I developed in 2013 due to the interest above. The research is now published.
Abstract of Paper “The Bottom-Up Formation and Maintenance of a Twitter Community: Analysis of the #FreeJahar Twitter Community” [Link to Article]
Purpose – The article explores the formation, maintenance and disintegration of a fringe Twitter community in order to understand if offline community structure applies to online communities
Design/methodology/approach – The research adopted Big Data methodological approaches in tracking user-generated contents over a series of months and mapped online Twitter interactions as a multimodal, longitudinal ‘social information landscape’. Centrality measures were employed to gauge the importance of particular user nodes within the complete network and time-series analysis were used to track ego centralities in order to see if this particular online communities were maintained by specific egos.
Findings – The case study shows that communities with distinct boundaries and memberships can form and exist within Twitter’s limited user content and sequential policies, which unlike other social media services, do not support formal groups, demonstrating the resilience of desperate online users when their ideology overcome social media limitations. Analysis in this article using social networks approaches also reveals that communities are formed and maintained from the bottom-up.
Research limitations/implications – The research data is based on a particular dataset which occurred within a specific time and space. However, due to the rapid, polarising group behaviour, growth, disintegration and decline of the online community, the dataset presents a ‘laboratory’ case from which many other online community can be compared with. It is highly possible that the case can be generalised to a broader range of communities and from which online community theories can be proved/disproved.
Practical implications – The article showed that particular group of egos with high activities, if removed, could entirely break the cohesiveness of the community. Conversely, strengthening such egos will reinforce the community strength. The questions mooted within the paper and the methodology outlined can potentially be applied in a variety of social science research areas. The contribution to the understanding of a complex social and political arena, as outlined in the paper, is a key example of such an application within an increasingly strategic research area and this will surely be applied and developed further by the computer science and security community.
Originality/value – The majority of researches that cover these domains have not focused on communities that are multimodal and longitudinal. This is mainly due to the challenges associated with the collection and analysis of continuous datasets that have high volume and velocity. Such datasets are therefore unexploited with regards to cyber-community research.
Keywords – Social network analysis, big data, twitter, online communities, social media, multimodal network, longitudinal network, freejahar, centrality measure
Four other research papers describes the process:
- Ch’ng E. (2015) The Bottom-Up Formation and Maintenance of a Twitter Community: Analysis of the #FreeJahar Twitter Community, Industrial Management & Data Systems 115(4), p.612-624. [Link to Article]
- Ch’ng E. (2015) Social Information Landscapes: Automated Mapping of Large Multimodal, Longitudinal Social Networks, Industrial Management & Data Systems, Vol. 115 Iss: 9, pp.1724 – 1751 [Link to Article]
- Ch’ng E. (2015) Local Interactions and the Emergence and Maintenance of a Twitter Small-World Network, Social Networking 4(2), p.33-40. [Link to Article]
- E. Ch’ng (2014) The Value of Using Big Data Technology in Computational Social Science, The 3rd ASE Big Data Science Conference, 4-7 August 2014, Tsinghua University Beijing China. [Link to Article]