Although Salafism has a long history in the Muslim communities, Salafism, mostly due to its violent extremist side, has gained visibility in the public opinion with the catastrophic September 11 terrorist attacks. The entry of Salafist networks in Turkey could be dated back to the early 1990s, however, Salafist ideas have been spreading in Turkey especially after the outbreak of Arab Awakenings and the Syrian conflict. Global Salafism and its’ national forms have been widely studied in the literature, but there is a lack of exploratory and explanatory analysis on Turkish Salafism when compared to other Middle Eastern countries.
This paper aims to study Turkish Salafism on Twitter-sphere. The main research question is how Turkish Salafis represent themselves on Twitter. Sub questions could be listed as follows: what is the nature of their ecosystem whether centralized or decentralized, which subgroups of Global Salafism show existence on Twitter, which subgroup dominate the ecosystem, what are the most referenced websites and social media platforms in their tweets, and which themes come to the forefront in their tweets.
The epistemological stand of this study is interpretivism. Ontologically, interpretive epistemology connects with objective and subjective. Therefore, human subjectivity is not detached from reality. This makes the knowledge produced contextual. So, the method of this research focuses on meanings and context. The interpretive nature of this research brings it a research-then-theory approach. Also, this paper uses a conceptual framework instead of the theoretical framework since Salafism, as a religious movement, is a complex social phenomenon.
This study focuses on the online sphere due to some reasons. At first, as the Salafi community is linked to terrorist groups, there is a security issue to study them in the field. Second, there is a scarcity of studies on the online sphere, hence this study aims to contribute to the literature by focusing on the online sphere. In the last decade, the online sphere has been a hub for social movements and social networks (Watts, 2007). They address both their supporters and potential supporters via social media and websites.
There are many platforms to study Salafis in the digital sphere. However, this paper limits itself only to the Twitter-sphere. Most Salafis use Twitter as a way of conveying their messages to the public. Facebook, Instagram, and other platforms are less common when it comes to the Turkish context. Besides, collecting data from Twitter is relatively easier than other platforms. And, twitter data, mostly text data, tell us about their network and their worldview. Studying twitter data allows us to take a big picture of the community through their own eyes. The data are created by themselves in their natural environment, the internet (Lazer et al., 2009).
Two methods will be used to explore twitter data. The first one is social network analysis in Twitter-sphere and the second one is quantitative text analysis on obtained text data from users. In general, social network analysis tells us about the important actors in the network, the level of connectedness, the existence of sub-communities in the network, and the sphere of influence of the network. In addition to network analysis, text analysis tells us about the worldview of the community, highly referenced websites, authors, and hashtags. In-text analysis section, themes are generated based on statistical results, and selected tweets regarding these themes provided with their critical discussions. Considering distinct features of these two methods, a combination of them allows us to grasp more about the nature of online communities.
There are three stages of methodology; data collection, data preparation, and data analysis. Data was collected on October 2018 by using a Python package which allows us to retrieve data from Twitter. Python is a common programming language among software engineers and data scientists. However, data preparation and analysis were made by using R programming language. As R language is mostly preferred by social scientists, R packages were used for text cleaning and analysis. The codes used for text analysis will be added as an appendix to this study. Moreover, Gephi application was used to visualize the network data and to calculate metric results. This study prefers to use Gephi, Python and R, as they are all open-source, free and user-friendly platforms.
Regarding ethical issues, Twitter Social Network Analysis is a widely used method among both academics and researchers. Twitter’s terms of service inform the users that the data created in this space will be shared with the public. The users implicitly give their consent to the researchers to use their data. However, the anonymity of collected data and the privacy of users should be protected.
References
Watts, D. J. (2007). A twenty-first century science. Nature, 445(7127), 489–489.
Lazer, D., Pentland, A., Adamic, L., Aral, S., et all. (2009). Social Science: Computational Social Science. Science, 323(5915), 721–723.