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#MyJihad Contested Space in the Public Sphere

Team Members

Daan Smith

Ezgi Akdag

Wannes Sanderse

Pascal Janssens


The Hashtag #Mijihad started out as an ad campaign in December of 2012. It was created by Ahmed Rehab in response to a controversial ad campaign by counter-jihad activist Pamela Gellar. Geller tried to run the following ad campaign in the New York Subway system:

It was rejected by the Metropolitan Transportation Authority, but after a federal judge overruled the ban citing the First Amendment right to free speech, the advertisement campaign ran in September 2012. It sparked a widespread debate in Muslim communities. Ahmed Rehab, the executive director of the Council on American-Islamic Relations noticed that the debate was centered on the word “savage” and not on the word “Jihad”. He described it as an indication that the Muslim community needed to be more pro-active in promoting the peaceful interpretation of their religion. He posted a story on Facebook about his sick grandmother and how she described her battle with her health as her Jihad. Rehab then started collecting money to run an ad campaign showing this positive view of the word Jihad, Jihad as overcoming a personal struggle.

Pamela Geller responded by running an ad campaign in the same style, but describing acts of terrorism:

In both ad campaigns the hashtag #myjihad is used. In this project we zoom in on the specific hashtag #myjihad because it is an example of a contested space.


The Myjihad hashtag is being hijacked by counter-jihad groups. An initiative that started off to give a positive image on Jihad, has been taken over by groups which use their campaign as counter-jihad propaganda. In this research we pose the following question: How can we characterize the hashtag Myjihad as a contested space?

The question aims at identifying different groups who use the hashtag Myjihad in different ways, for different purposes. What kind of tweets do each of these groups use? This research focuses on one hashtag, therefore, it is important to question what kinds of methods we can use to analyse this platform-specific feature. How do the different features of Twitter, such as the hashtag, the retweet and the tweet itself, relate to each other? By combining these platforms specific aspects we will give a characterization of the Myjihad hashtag as a contested space. Furthermore, we want to identify certain sentiments that each of the groups are using. Our hypothesis is that the Myjihad organization tries to give a more positive meaning to the word jihad, while the anti-Islamist groups emphasize the negative side of it. Is it possible to analyse sentiment, to make a distinction between these two groups? The following section will discuss different methods for Twitter analysis and explain the steps we took to get answers to the questions we have posed.


The Anti Defamation League describes Cyberhate as “any use of electronic communications technology to spread anti-Semitic, racist, bigoted, extremist or terrorist messages or information”( 2010, 44). Such is the case for the counter-jihad movement on Twitter under the hashtag myjihad started by Pamela Geller. The electronic communications technologies, as mentioned by the Anti Defamation League, include “the Internet (i.e., Web-sites, social networking sites, “Web 2.0” user‐generated content, dating sites, blogs, online games, instant messages, and E-mail) as well as other computer- and cell phone-based information technologies (such as text messages and mobile phones)” (Anti-Defamation League 2010,1). Why do individuals, institutions and/or organizations, (re)use the #MyJihad in favor of ‘hate’? The motivation of hate and terror groups for using Internet is explained as to recruit people, communicating with each other and organizing, selling merchandise to raise money (Gerstenfeld, Grant and Chiang 2003, 30). According to Bloomberg Technology’s article, with Twitter usage, the hate and terror speech in online media increased by 30 per cent during the year 2012 (MacMillan 2013). Hashtags along with mentions and retweets can be used as a tool employed by users to further articulate their opinions on a particular topic. Especially, the hashtags increase the participation level as it rises the invisibility of a topic by allowing users to reach the curated tweets posted about the very subject. This can be explained by the following quote of Bruns and Burgess

In the years since 2007, through widespread community use and adaptation, the hashtag has proven itself to be extraordinarily high in its capacity for “cultural generativity” ... and has seen a proliferation of applications and permutations across millions of individual instances – ranging from the coordination of emergency relief... to the most playful or expressive applications (as in Twitter ‘memes’) or jokes...; to the co-watching of and commentary on popular television programs ...; and of course...the coordination of ad hoc issue publics, particularly in relation to formal and informal politics. (2011, 2)

Bruns and Burgess further claim that since the users can respond and react to the issues which is accumulated and categorized under hashtags, they are useful for creating ad-hoc publics (2011,7). Because of this fact, the hashtags provide foreknowledge when understanding and analyzing the different reactions to ‘massively shared experiences’ (Bruns and Burgess 2011, 7). For example, when Usain Bolt is running while the Olympics are going on, a #Bolt can provide potential foreknowledge and can cause people to share an experience with each other via Twitter. Furthermore, people can reflect and share their offline ‘ massively shared experiences’ such as natural disasters and protests with online audience.

From January the 12th 2013 till the end of this same year 424.623 tweets have been tweeted which contain the word “myjihad”, either in the form of a hashtag or as word within the tweet. In order to retrieve this dataset of tweets we have used the Twitter Capture and Analysis Toolset (TCAT), developed by the Digital Methods Initiative. The TCAT allowed us retrieve tweets, retweets, used URLs and hashtags. To identify different groups of tweets and to analyze these groups, we needed to categorize the tweets. Since there is a large amount of tweets (424.623), we need to limit the dataset so we could manually categorize them. Therefore, we only focus on the most retweeted tweets. As Poell and Darmoni argue in their article “retweeting effectively highlights the most relevant messages for uses. Anyone who retweets a message forwards it to all their followers, which can sometimes be thousands of people. Hence, each retweet substantially increases the range of the original tweet.”( 2012) Since our research focuses on the groups who try to (re)claim the hashtag, including only the top retweets gives an indication to which group is the most successful. The more a tweet is retweeted, the more visible that particular tweet is. In this sense, the most retweeted messages in this dataset are the most visible, and arguably the most popular, tweets of the hashtag “myjihad”. For these reasons, we have used the TCAT in order to retrieve the ten most retweeted tweets of every month in the year 2013. This way we could question whether the MyJihad campaign from was successful or not, whether it is used in a positive way. Additionally, since we retrieved monthly data of top retweets, we were able to identify changes of popularity of certain groups. However, for us to do this, we needed to create a method for categorizing the different groups.

We have made three different categories of the top retweets. ‘Positive Jihad’, ‘Counter-Jihad’ and ‘Other’. On the website of Myjihad they explain that #MyJihad is about:

[f]or Muslim and anti-Muslim extremists (who ironically are on perfect agreement), Jihad is synonymous with terrorism, blowing up things, and spilling innocent blood. This campaign is about reclaiming our faith and its concepts from these extremists, both Muslims and anti-Muslim, as well as their cheerleaders and clap-trappers, all of whom have for too long now effectively hijacked and dumbed-down the conversation about Islam and Muslims. ( MyJihad)

Since the campaign aims at given a positive meaning to the word “jihad”, which originally means “the struggling way of God” (Myjihad), we have categorized the tweets which tweet with the hashtag “myjihad” in a positive way as “Positive Islam”. An example for this type of tweet is:

the prophet taught us to co-exist with people of different affiliations approaches and beliefs. respect all. #myjihad’ (r_yurtsever 2013).

The other category, “Anti-Islam”, contains tweets who use the hashtag “myjihad” to tweet about Islam in a negative, most of the time referring to themes as terrorism, women rights and crimes. An example for this type of tweets is:

#MyJihad - RT till EVERYONE gets it - #WOOLWICH MURDER was the work of MUSLIMS fighting for ISLAM to commit JIHAD as commanded by the QU'RAN’ (ARDentadmirer1 2013).

The final category, which is relatively small, consists of tweets which do not fit in any of the previous category. Most of the these tweets use the “myjihad” hashtag and refer to a broken link, therefore these tweets could not be identified as positive or negative toward Islam.

After identifying the different groups of tweets, “Positive Islam” and “Anti-Islam”, we have done a sentiment analysis of the retweets. By doing this analysis we wanted to find out whether these different groups tweet with different sentiment. Sentiment analysis is defined as the examination of the sentiments in a text, which came into being a result of the topics discussed online (Thelwall, Buckley and Paltoglou 2012). In another article, Thelwall, Buckley and Paltoglou argue that the sentiment analysis can be employed to detect the extent of a text’s subjectivity, ‘the polarity’ of a text depending on the negative and positive comments and also strength of the emotions (2011, 408). SentiStrength, the tool we have used, is a sentiment-strength detection tool developed for the assessment and evaluation of the emotions articulated in a text both in a binary form as positive or negative or in a trinary form neutral,positive and negative ( SentiStrenth). If this software could identify different groups because they use different sentiment, one tweeting more positive and the other tweeting more negative, it would prove that these two groups could be identified in an automated way.

As explained above, the TCAT allowed us to retrieve the used hashtags. We have used the TCAT to retrieve the most used hashtags in the same tweet. This way we could create a network graph, by using Gephi. We have made several network graphs of co-used hashtags of several of months in the year 2013. The reason we did this is to understand whether there is a change visible of hashtags that are used in correlation with the “myjihad” hashtag. Additionally, some of the hashtags refer to certain organizations or movements, such as the #EDL which refers to the English Defence League.


In general, the amount of tweets involving around the #MyJihad has decreased. Graph 1 shows the total number of tweets from our dataset. On April 27th, the graph shows a significant increase in the number of tweets. In order to understand the peak in the graph, we analyzed the tweets of this particular day and found that it was a result of one tweet which was massively retweeted. The tweet was from Eric Allen Bell who is part of the counter-Jihad movement. His tweet is about a mosque which is being built in the United States and has been retweeted over 7000 times.

#MyJihad #tcot YES I DID TELL THE TRUTH ABOUT #Islam to 2 MILLION PEOPLE: (@EricAllenBell).

The fact this tweet, which is clearly anti-Islam, is retweeted in this scale, already shows that there is much support for anti-Islamic tweets.

Graph 1 Total tweets over time

Graph 2 also shows this trend of “anti-Islam”. The graph shows the top ten most retweets of every month in 2013. As expected, on April, the most retweeted tweets were anti-Islam. The graph shows that until July, both positive and anti-Islamic tweets were being retweeted. However, as from July, there were almost no positive retweets about the Islam anymore. The tweets itself do not show a clear explanation of this trend. It could relate to offline events such as the Boston Bombing or the Woolwich Murder. However, we were unable to identify a strong relation to these events and the tweets.

Graph 2 Top 10 retweets over time

The results of the sentiment analysis of the top retweeted tweets are shown in graph 3. Our hypothesis was that the group “positive Islam” would be labeled with positive sentiment, while the “anti-Islam” group would be labeled with a negative sentiment. If this was true, graph 2 and graph 3 should show similar results: until July there would be a mix of positive and negative sentiment and from that moment on, there would be mostly negative sentiment. However, such a relation cannot be found. The sentiment analysis does show that the tweets are mostly written in negative sentiment. Even if a tweet has a positive attitude towards Islam, it is still written in negative sentiment. For example,

#myjihad :cameron mohammed: armed hate crime victim doesn’t shoot back even in self defense. #islamaphobia (@myjihadorg 2013).

Although the Myjihad campaign was meant to tweet positively in order to change the connotation of the Jihad, the users tend to tweet about negative aspects.

Graph 3 Sentiment Analysis

The decline in tweets which had a positive attitude towards Islam and the word Jihad was noticed by the Myjihad organization. They started a campaign on their facebook page to try and retake the hashtag #mijihad and post positive messages about what jihad means to them.

﷽ We have officially begun our campaign! Tweet your own real examples of jihad with the tag #MyJihad all day today! (@MyJihadOrg 2013)

Graph 4, which includes the top 20 retweeted messages on this particular day, on July 5th, show the effects of this campaign. Over 75% of the messages are labeled as “positive Islam”. However, as graph 2 shows, the campaign does not have a long term effect.

Graph 4 July 5th

Co-Hashtag Graphs Gif File

In order to see how #MyJihad has changed throughout the year we also looked at its relationship with other hashtags. Approaching the hashtag on just a textual manner and out of the context, out of the tweets, would be problematic since people could have used the hashtags on either a neutral, positive or negative way. We have created several Co-Hashtag Graphs, in order to explore the relationships between several hashtags. For example, if #MyJihad is surrounded by terms such as ‘islamophobia’ and ‘terrorism’ one can expect the hashtag to be used in a negative context.

In the beginning of last year #MyJihad involved both terms as Muslim and Jihad, relatively neutral terms, mostly used in favor of the hashtag. Additionally, the term Islamophobia is visible in the first month. Whereas during the last month of 2013, the term Muslim has almost disappeared from the map and the #MyJihad is completely surrounded by relatively negative terms such as Femen (an Anti-Christian and Anti-Muslim organization), Filthyislam (self-explanatory), Tcot (Top conservatives on Twitter), EDL (English Defence League). This shift confirms how the #MyJihad was (is) hijacked, by mostly counter-jihad groups. This might be an indicator of the decrease of the total usage of the hashtag, there is another negative connotation that dispirits people that would like to tweet about MyJihad on a neutral, or a positive way. Also, the hubs surrounding the hashtag are smaller, compared to previous months, which can be related to the aforementioned claim that there is a general decrease in the amount of tweets. In the last month of the year, the term hdl (Hindu Defence League) turns up next to the #Myjihad. This is also noticeable in the uses of URLs (see the attachment), where websites of Hindu organizations are being used more often in the end of the year.


In the book The Net Delusion Morozov writes how the internet is seen as a tool in favor of the oppressed rather than the oppressor. In general, there was great optimism about the positive implications the internet would have in terms of freedom of speech and equality in the early days of the internet. While elaborating on the work of Habermas in the work Habermas’ Heritage: The Future of the Public Sphere in the Network Society, Boeder (2005) writes about the potential implications of digital communication technologies, and their implications for rights such as freedom of speech.

The author mentions the translated term ‘public sphere’ (Öffentlichkeit) as coined by Habermas, and how this is a discursive ‘place’ where ‘private people come together as a public’ (27). Through rational discussions in coffee places, buses, bus stations or even Twitter (and so forth) one can perform and exercise an ideology-free public opinion. The public sphere is an overarching ‘abstract forum’ (Boeder), that transcends all ‘physical’ places, while at the same time these physical places contribute to the public sphere. One can practice democracy, freedom of speech and other rights respectively on these domains. Habermas’ critique starts with the growing influence of consumerist society and how it can inhibit such practices. It’s called the commodification of the public sphere and can cause discussions to be ‘pre-empted’ (Boeder), the latter is meant to describe how innovate communication technologies can impede, or retain, a certain rational discussion from going to a certain direction. It can be exemplified by Facebook’s Like Button and its positive character, or the short amount of characters that one can use on Twitter.

It is hard to capture the full extent of the ‘positive side’ of the debate within a single tweet. For example, the groups in favor of #MyJihad have to illustrate a lot to describe their side of the story a tweet like:

#myjihad is to let as many people know that muslims are respectful and kind (@myjihadorg 2013).

It does not immediately explain what they mean by Jihad and how its meaning is different than how it is often portrayed in the media, it is multi-interpretable. Whereas the counter-jihad groups touch upon gut feelings, mostly short statements, which is strongly facilitated by a platform such as Twitter.

#MyJihad - Qu'ran FORBIDS you to be friends with us PROMOTES our subjugation CALLS for you to kill us (@MuhammadThePig 2013).

Twitter seems to be an suitable platform for bold, hardline statements, like the ones we find from the Anti-Islamic groups.

In this research we have posed the question how can we characterize the hashtag Myjihad as a contested space? Our data shows that there is clear distinction between two groups tweets, one which is positive towards Islam and one who is negative. The Myjihad hashtag is characterized as a campaign of the MyJihad organization which is trying to give a positive meaning to word Jihad. As our data has shown, this attempt has not been successful. At one moment, in July, the organization even tried to reclaim the hashtag since it has been dominated by anti-Islamic groups. However, this attempt has been unsuccessful. In all of our data, the hashtags, the top retweets and the most used URLs, it becomes visible that from July on, the hashtag is almost completely dominated by anti-Islamic tweets. Furthermore, we can characterize the tweets as negative, even the ones which are positive towards Islam. Although the positive attitude of the MyJihad organization is not reflected on the dataset. Apparently, the 140 characters are enough for a hurtful one liner and an inflammatory meme, but most of the time not for a well structured argument to debunk preconceived conceptions.

Attachment 1:


Anti-Defamation League. “Responding to Cyberhate: Toolkit for Action”. A 2010. 16 January 2014. <>.

Bruns, Axel, and Jean Burgess. “Researching News Discussion on Twitter.” Journalism Studies 13.5-6 (2012): 801-814.

Bruns, Axel and Jean Burgess. “The Use of Twitter Hashtags in the Formation of Ad Hoc Publics.” 6th European Consortium for Political Research General Conference. (2011): 25–27.

Boeder, Pieter. “Habermas’ heritage. the future of the public sphere in the network society.” First Monday 10.9 (2005) 16 january 2014. <>.

English Defence League. 2013. Web. 16 January 2014. <>.

Gerstenfeld, Phyllis B., Diana R. Grant, and Chau‐Pu Chiang. "Hate online: A content analysis of extremist Internet sites." Analyses of social issues and public policy 3.1 (2003): 29-44.

Habermas, Jürgen. The structural transformation of the public sphere. Cambridge: Polity Press, 1989.

Macmillan, Douglas. “ Twitter Aids Rise of Web-Based Hate Forums, Report Finds”.Bloomberg

Technology. 7 May 2013.Web. 16 January 2013. <>.

Morozov, Evgeny. The Net Delusion: The Dark Side of Internet Freedom. London: Penguin Group, 2011. 2012. Web. 16 January 2014. <>

Phillips, Patrick. “Jack Dorsey: Twitter Complements Traditional Media” I Want Media.2004. Web. 16 January 2014. <>

Poell, Thomas and Kaouthar Darmoni. “Twitter as a multilingual space: The articulation of the Tunisian revolution through #sidibouzid.” European Journal of Media Studies 1.1 (2012): 14-34.

Rogers, Richard “Debanalizing Twitter: The Transformation of an Object of Study” Proceedings of ACM Web Science 2013 (May 2013): 1-9 < >

Thelwall, Mike, Kevan Buckley, and Georgios Paltoglou. "Sentiment in Twitter Events." American Society for Information Science and Technology 62.2 (2011). Statistical Cybermetrics Research Group, University of Wolverhampton.

Thelwall, Mike, Kevan Buckley, and Georgios Paltoglou. “Sentiment Strength Detection for the Social Web.” Journal of the American Society for Information Science and Technology 63 (2012): 163–173.
Topic revision: r5 - 19 Jan 2014, PascalJanssens
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