#GazaUnderAttack: Twitter, Palestine and Diffused War

***Pre-print version

Eugenia Siapera, eugenia.siapera@dcu.ie

Graham Hunt, graham.hunt@dcu.ie

Theo Lynn, theo.lynn@dcu.ie

Introduction

Since Hamas took over Gaza in 2007 and Israel imposed a land, airand sea blockade, there has been continuous violence between the two sides. Since 2008, the Israeli Defense Forces (IDF) has launched four major operations against Hamas in Gaza, including Operation Hot Winter (2008); Operation Cast Lead (2008-2009); Operation Pillar of Defense (2012) and Operation Protective Edge in the summer of 2014. Operation Protective Edge was launched bythe IDF on July 8, 2014 against Gaza with two objectives, ‘restoring security to Israeli civilians living under Hamas rocket fire’ and ‘dismantling the Hamas tunnel network used to infiltrate Israel’ (IDF, 2015). By the end of the Operation on August 26, 1,475 Palestinian civilians were killed, while in Israel the fatalities were 5 (OCHA, 2014). The devastation that this operation brought to Gaza was equivalent to the Operation Cast Lead (OCHA, 2009); however, the conditions under which Operation Protective Edge took place were significantly differentto those of previous operations in at least one important parameter: that of communications.

In the years since Operation Cast Lead, social media platforms were diffused more widely and used more systematically in war (Berenger, 2013). The communicative environment within which Operation Protective Edge took place is very different to the oneof the previous operations. Recent theorizing has attempted to apprehend the new ecosystem in terms of a hybrid media system

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(Chadwick, 2013), pointing to its increased complexity, the constant streams of information circulating between and across distinct media forms, and the overall dynamism of the various elements of the system. More directly concerned with war and conflict, Hoskins and O’ Loughlin (2010) speak of the emergence of ‘a diffused war’, which involves the mediatization of war, theproliferation of intervening variables between cause and effect, and the increased uncertainty for decision makers. What these views have in common is firstly an understanding of the new communicative environment as a dynamic system, and secondly, the view that the communicative dimension of conflict cannot be thought of as outside of or separate to the actual conflict itself.

It is within this dynamic and fluid communicative ecosystem that the latest IDF operation took place. The present article focuses on one of the many elements of this ecosystem, namely Twitter. Twitter’s role as both a news medium (Kwak et al., 2010) and a system of social awareness (Hermida, 2010) render it a focal point within the new ecosystem. Additionally, Twitter can be seenas an entry point to, and a thumbnail for, the broader communicative ecosystem, given its openness and wide diffusion, as well as its ability to host other media and other media forms.

Focusing on Twitter, the article seeks to identify and understandhow the Gaza attack over the summer of 2014 was mediated. The article relies on an analysis of almost 3 million tweets posted in the period of the Operation Protective Edge. . The tweets under analysis were collected on the basis of specific hashtags and screen-names.. The aim of the analysis is to recognize trendsand to examine the overall contribution and role of Twitter in this particular iteration of the conflict. This entails an identification of the main communicators, the emergence of any new actors, the positions and sentiments expressed on and the overall direction of the communications.

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The article begins with a consideration of the broader communicative environment in war; it then moves on to discuss more specifically the mediation of the Palestinian conflict, before discussing the methodology and findings of the present study.

Mediatization of war

A turning point for media in war has been the war in Vietnam, which was understood as the uncensored war (Hallin, 1989). While the media had been used as propaganda vehicles in earlier wars, in the war in Vietnam they were ostensibly free to report, and this, according to t Nixon (1985), cost the US the war. . The next turning point was the first Gulf War in 1991, which gave rise to the CNN effect, or ‘the idea that real-time communications technology could provoke major responses from domestic audiences and political elites to global events’ (Robinson, 1999: 301). The centrality of television and the spectacularization of war, which is then made to fit the requirements of television and the needs of the 24/7 news cycle, led Baudrillard (1995) to argue that the event of the Gulf War was a simulacrum of war, a collection of images operating spectacle and devoid of any historical meaning or significance. These discussions and arguments may be seen as part of the first phase of the mediatization of war, which was predominant in the mid to late 20th century (Hoskins and O’ Loughlin, 2010). However, the early 21st century witnessed the rise of new kinds of media and technologies, which in turn ushered in the second phase of mediatization. In this second phase, the dynamics between the main war actors are different and the outcomes unpredictable and ambiguous. This section will explore these arguments, based upon and expanding Hoskins and O’ Loughlin’s (2010) notion of the two phases of mediatization and the rise of diffused war.

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The distinction between the two phases of mediatizationi of war can be seen as corresponding to the distinction between differentmedia ages. Specifically, Poster (1995) made the argument that the internet heralded a second media age, in which the networked and decentralized structure of the media is reflected in the personalized, interactive and participatory practices of users. If the first media age was characterized by one-to-many centrallycontrolled communication that influenced consciousness, the second media age is characterized by many-to-many communication that evades state control and that influences people’s individualexperiences of time and space (Holmes, 2005: 10). While the paradigmatic medium of the first media age was television and broadcast, in the second media age the paradigmatic medium is theInternet.

In the same vein, the two phases of mediatization of war posited by Hoskins and O’ Loughlin (2010: 19) make similar distinctions; the first phase is dominated by ‘Big Media’, mostly Western in origin, spectacular mass warfare, and predictable and measurable actions and effects, while in the second phase we can observe intense international competition for news, weaponized media, anddiffused audiences. Diffused war is therefore the term used by Hoskins and O’ Loughlin to apprehend the ‘emerging paradigm of war’ (2010: 4) in which the second phase of mediatization diffuses causes and effects, and creates uncertainty for policy makers. In diffused war, the relationships and links between the ‘trinity of government, military and publics’ (op. cit.: 18) are unpredictable, forcing them to deal with unexpected feedback and to learn news ways of managing information. In short, Hoskins andO’ Loughlin argue that diffuse war involves a reordering of the relationships between government, military and publics, and that this is due to the ways in which war is mediatized. More recently, O’ Loughlin and Hoskins (2015) developed the notion of ‘arrested war’ to point to the ways in which the mainstream mediaremediate social media contents, ‘arresting’ or capturing certain

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events or issues or images and more or less ignoring the remaining. O’ Loughlin and Hoskins make the argument that in the chaotic and fluid world of the current media system, mainstream media emerge as sources of order and intelligibility. In this manner, the notion of diffused war, with multiple competing voices competing in a complex media landscape with uncertain outcomes, offers a holistic analytical perspective, complementingand building upon work on the role of new media in political protest (e.g. Castells, 2012)

Two further concepts from Hoskins and O’ Loughlin may illustrate further the notion of uncertainty in diffused war; these are mediality and vector. Firstly, the concept of mediality (Crocker,2007; Grusin, 2007) signals a shift from the representational to the relational. The questions posed by the notion of mediality include: how do media relate to readers/users in terms of their own lived experiences, what is the ‘noise’ they add to the connection. This takes away the focus from examining the contentsof communication towards an examination of the relationships fostered by different media. Secondly, the term vector comes fromthe work of Virilio (1986) and Wark (1994) and refers to the connections between objects or points made by the media across, between and within discrete geographical (topological) spaces. Intheir work, Hoskins and O ‘Loughlin use it to refer to the disruption of notions of distance and proximity but also to the creation of fluid connections between discrete points or nodes. In the present article, we make use of this conceptual apparatus to interrogate the role of a specific media platform, Twitter, inthe war in Gaza in the summer of 2014. But this further requires a discussion of the histories of the mediatization of Gaza and the Palestinian question.

Gaza and Palestine: Phases of Mediatization

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Applying the phase model of mediatization to the protracted conflict is revealing of the dynamics at work, which show both continuities and discontinuities. The first phase of mediatization can be understood through the work of Wolfsfeld (1997) who looked at the conflict during the Oslo peace negotiations in the mid-1990s. Wolfsfeld proposed the political contest model, which focused on antagonists and journalists/mediaorganizations as relevant actors, arguing that the varied role played by the media in political conflict can be attributed to the extent to which antagonists manage to gain control over the media and political environment.

For the Palestinian conflict in the mid-1990s Wolfsfeld found that despite the tremendous advantages of Israel, its ability to gain control over the media frames of the conflict were undermined by the events of the Palestinian intifada in December 1987. During this event, the media played the role of an equalizer between the two antagonists, and frames supporting the intifada were widely diffused, leaving Israel unable to control or regulate the flow of information and to gain support of its suppression of the intifada.

In contrast to Wolfsfeld’s arguments, Philo and Berry (2004) in their analysis of the ways in which the conflict appeared in the British media (BBC and ITV) report that the media coverage is skewed in favour of Israel. Notwithstanding the difference in their findings, both studies focused on the mass media, as the only source of information on this conflict. Since this was the only source, control over access and frames or contents was seen as the most significant factor. Following Hoskins and O’ Loughlin, the second phase of mediatization is expected to signala shift in this, towards a more diffused model of communication, where information is flowing from all sides, and is impossible tocontrol. However, in the case of Palestine, there have been significant and successful attempts to control information flows.

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These have taken two forms: one is to control internet access in the Occupied Territories and the second is to restrict journalists’ access to Palestinian sources. Specifically, journalists’ access to Gaza was restricted through the 2008-2009 conflict in Gaza (Amnesty, 2008). Although formal restrictions have been lifted, there are no security guarantees for journalists effectively limiting their access to Gaza. Additionally, there are reports that Hamas exercises control overwhat reporters who make it into Gaza can report, although this isa contested issue (Al-Monitor, 2013 ; Haaretz, 2014). Moreover, Tawil-Souri (2012) discussed the ways in which Israel has sought to restrict the internet access of Palestinians through restrictions of land-lines, mobile and internet infrastructures. In more recent work, Tawil-Souri and Aouragh (2014) speak of ‘Intifada 3.0’ to refer to the growing Palestinian resistance on and about the internet. As Tawil-Souri and Aouragh (2014) report,this resistance is also directed against Israeli-imposed internetrestrictions; for example, the ‘Enough Walls’ campaign protested against the imposed limits on bandwidth. For Tawil-Souri and Aouragh (2014) Intifada 3.0 needs to challenge not only the Israeli territorial colonialism but also the cyber-colonialism imposed on Palestinians and their use of the internet. This bodyof work shows that controlling media access continues to be an on-going issue in the second phase of the mediatization of Gaza.

On the other hand, research has shown the proliferation of content on Palestine in online spaces, which allow Palestinians and their supporters to move beyond the constraints and barriers of mainstream media. As Aouragh (2008) put it, ‘the internet technology authorized a space to narrate the experience of suffering and struggle; but also to mobilize local and transnational activism and help structure political agency from below.’ Najjar (2010) studied the way in which social media were used by Palestinians and their supporters during the operation Cast Lead (2008-2009), showing the debates and heated exchanges

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that were going on but arguing that more broadly, providing ‘the opportunity for all the Palestinians in the diaspora, other Arabs,Muslims, and international supporters to reach various audiences interested in the other version of the story or curious to see events, images, and comments that do not appear in the international mainstream media.’ Tawil-Souri and Aouragh (2014) refer to the various ways in which Palestinian activists use the internet, for example, through the Active Stills or Shooting Back campaigns that documented Israeli violence against Palestinians. Siapera (2010) discussed the emerging networks surrounding Palestine, including media, NGOs and bloggers, who keep the memory and current problems of Palestine alive. Similarly, Etlinget al. (2010) report that Palestine unites the whole network of the Arab blogosphere. In more recent work, Siapera (2014) has looked at the ways in which Twitter has allowed a bottom up organizing that eludes control from political organizations, suchas Hamas and Fatah, or from the occupying forces of Israel, and which seeks to form international alliances and solidarities.

The second phase of mediatization for Palestine shows that there has been a shift towards allowing Palestinians to tell their stories and to directly contribute to the narration of Palestine and to constructing Palestinian identity, even if contested both from within the Palestinian and Arab communities and from outside, notwithstanding the Israeli-imposed internet restrictions and surveillance. These observations are supported by recent work on social media and war, which point to the proliferation of various sources of communication and the loss ofcontrol of the war communications (Berenger, 2013). However, as more and more stories, experiences and contestations accumulate, the communicative environment acquires more nuances and shades. Moreover, it may experience shifts and spikes, especially when itcomes to events such as Operation Protective Edge, when the communicative environment may expand in unpredictable ways. It

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becomes therefore important to identify who the contributors may be, and what is the kind of resonance that their communications have in the broader communicative environment.

The present article will address these questions, focusing on a specific communicative platform, Twitter. Using Twitter data thisarticle looks at two related aspects, the mediality of Twitter and the vectors of Twitter communications on Gaza. Mediality willallow an understanding the specificity of Twitter and its contribution. Identifying the vectors will help set the ‘points of flight’ or the parameters within which the communications on Gaza take place; these can be geographical, ideological and affective. While ‘big data’ studies can be quite descriptive we have made use of these two analytical constructs to make sense ofour findings.

More specifically, these are operationalized as follows. We take mediality to refer to the specificity of Twitter, the ‘noise’ that it brings to the discussion (Crocker, 2007); this is examined here in terms of the actors that acquire prominence within Twitter’s communicative environment, the hubs and networks, the ways in which the conversation is framed and circumscribed through hashtags, and the parts of the conversationthat are shared through retweets. Secondly, we operationalize vectors as the geographical parameters of Twitter’s communicationon Gaza, including an analysis of geotags and languages, along with the contents and the affective direction of the conversations, operationalized as sentiment. Through these two concepts, we are seeking to trace the contours of the debate on Gaza and Palestine, especially in times of escalation, and through this to discussions on the mediatization of Palestine. The next section discusses the methodology used for this analysis.

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Data and Methodology

The study relates to the Twitter discourse relating to Operation Protective Edge between July 5 July and 26 August 2014. The data sampling process for this study uses keywords and hashtags relevant to the topic of interest. These were initially identified through initial searches using the desktop Twitter interface. These keywords and hashtags were then used to extract a 1% sample of tweets using Datasiftii, a data aggregator to identify co-occurring keywords and hashtags as well as influential screen-names. This allowed us to subsequently collectall relevant tweets while ruling out redundant content. For this study, we collected tweets that contained the key words and hashtags; Gaza, GazaUnderAttack, Hamas, Palestine, and Israel. Wealso extracted tweets from the following popular screen-names - IDFSpokersperson, AlqassamBrigade, ALQassamilitary, Tsahal_IDF, Intifada, Qassam_English, Qassamenglish, and qassamsms. 49,205,389 tweets were identified that related to the Operation Protective Edge. Of those tweets, 6% were original tweets (2,972,289) with the remaining 94% identified as retweets.

This data was then augmented to include language and gender classifiersrs, a social influence score (Klout) of each screen-name and a sentiment score for each tweet. These provide greaterinsight into the role and significance of the various categories of communicators. The tweets and their metadata were coded for their structural features such as links, hashtags, retweets, mentions, screen-names etc. This data was then consolidated and stored using Google Big Query and analysis was carried out using Tableau and the programming language R.

The study makes use of both descriptive analytics and content analytics as techniques for examining the information and contentin the dataset. This has been noted as appropriate method for analysis of online research (Neuman 1997, p.13; Anderson and

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Kanuka, 2003; Chae 2015, p. 5). The analysis involved the use of statistical and data mining techniques to develop and visualize descriptive statistics. Content analytics was carried out throughcross-referencing the content and structural features to identifyusage patterns (Jones, 2014). In this case word analysis, hashtaganalysis and sentiment analysis were used to extract intelligencefrom the dataset. For the sentiment analysis, a lexicon-based approach was used as it is best used for short lengths of text (Bollen et al, 2011;Gilbert and Karahalios, 2010; Tumasjan et al.2010). The methodology largely follows the approach for descriptive analytics and content analytics outlined by Chae (2014). One more issue is worth noting. When harvesting the data some 3-5 months after the ceasefire, we found that a lot of videos and pictures had been removed, links were no longer valid,and some accounts were suspended. We note below that Twitter’s suspension of accounts and removal of contents make it an actor/communicator in this war.

Findings

Descriptive AnalyticsAs discussed above, within the dataset of 49,205,389 tweets 6% (2,972,289) were original tweets with the remaining 94% (46,233,100) retweets. There were 254,857 unique screen-names mentioned in these tweets. We found that 126,035 different hashtags were used in the Tweets; approximately 65% (1,951,328 tweets) of the tweets did not contain a hashtag. Of those tweets that did use a hashtag, each tweet had an average of 2.6 hashtagsper tweet.

We identified 860,911 unique screen-names. On average each screen-name posted 3.5 tweets during the period of study. However, significant deviations were noted with a small number ofscreen-names making a considerable contribution to the number of tweets. To understand the types of users who were actively

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tweeting during this period, a term frequency count of Twitter screenames was undertaken. This is based on the self-reported descriptions by those screen-names. As shown in Table 1, the mainuser groups were students (12,490) and journalists (6,930). Religious beliefs were also frequently outlined in screen-name descriptions with ‘Muslim’ (6,382) noted as the most prevalent religion of tweeters. The most active screen-names (those producing the highest volume of tweets) are noted in Table 12. Following an analysis of the Twitter screen-name description (bio), it was established that nine of the top ten screen-names were activists; the remaining one screen-name was a news screen-name. On average, these screen-names were active on Twitter for 3years.

Using a gender classifier, (which could be determined for 40% of tweets) it was found that 67% of tweets were from males. A language classifier was used to augment the dataset and identify

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the language of the tweets. 143 different languages were identified. English was the dominant language representing 62% ofthe tweets. Spanish (12%), Indonesian (7%) and French (3.5%) also had a significant proportion of tweets. Further analysis wascarried out to identify the location of active Twitter screen-names. It was found that Twitter screen-names listing the United States (17.8%) and United Kingdom (13.9%) as their location dominated the conversation. A noteworthy volume of tweets was prominent from Indonesia, Venezuela and France. A heat map comparing the volume of tweets per country is shown in Figure 1.

Figure 1 A heat map of tweets

The Bot or Not? Classifier was used to classify the top 25 accounts as software robots (“bots”) or not. Social bots are not necessarily malicious, they may aggregate content from various news feeds however they can also be used to disseminate propaganda or unverified content (Ferrara et al. 2015; Cassa et al., 2013). Of the top 25 accounts, ten were categorised as

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humans, 7 indeterminate and two as bots. The remainder of the top25 accounts were suspended or terminated. The above analysis, while limited, suggests that there is evidence of some coordinated bot activity in this domain. The emergence of bot activity in such political domains has the potential to undermineTwitter as both an information and news dissemination.

As noted above 254,857 unique screen-names were mentioned in the original tweets. In total these screen-names were mentioned 1,567,726 times. Cha et al. (2010) notes that the number of mentions is a measure of influence. Table 4 shows the most mentioned screen-names among the original tweets.

Table 4 Most mentioned screen-names

URLs to content outside of the Twitter network was a popular feature of tweets within the dataset. The most linked to domains were YouTube (95,786) and Facebook (58,827). This was followed bythe major news organisation such as the BBC and CNN. 57% (1,683,899) of original tweets contained a URL. In total, 907,818 unique links were found in the original tweets. The top

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URLs mentioned in tweets are outlined in Table 5 with the most popular domains outlined in Table 6.

Table 5 Top URLs featured in original tweets

Table 6 Most linked-to domains

Content AnalyticsAs part of the study we undertook word analysis using term frequency and clustering. By analysing key and co-occurring termsand phrases in tweets, we can gain greater insight in to the topics of the discourse. To gain an understanding of the content,we look to identify the most popular hashtags, which provide an indication of topics discussed. We also carry out a sentiment analysis that provides insight into overall orientation (positiveand negative) and intensity (strong or weak) of opinions in the tweets.

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The co-occurring keywords were found to be predominately negativeand focused on the topics of crimes, children and hospitals. The most mentioned negative keywords included crimes, terrorists, death and destroy.

As discussed above, among the 2,972,289 original tweets in the dataset, 126,035 different hashtags were used. These hashtags were used 2,680,502 times. 65% of tweets did not contain a hashtag. The dominant hashtags observed were #Gaza, #Gazaunderattack and #Israel. Of those tweets containing a hashtag, 69% contained at least one of the top 3 mentioned hashtags. The most observed hashtags are outlined in Table 7.

Table 7 Most frequent hashtags in original tweets by volume

Sentiment analysis is used to examine overall orientation (positive and negative) and intensity (strong or weak) of opinions in text (Pang and Lee, 2011). It provides a measure of sentiment towards the topic of discussions and subjectivity in text (Pang and Lee, 2011). Twitter sentiment has been shown to reflect the landscape of the offline world (Bae and Lee, 2012). By analysing the Twitter sentiment we gain an understanding of

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the public attitude online towards the conflict and also a reflection of offline opinion. Given the controversial nature of politics and conflicts, it has been shown that a high level of diversity is expected in sentiment analysis (Stieglitz and Dang-Wuan, 2013). Figure 2 shows the dispersion of sentiment in the tweets.

Figure 2 Sentiment Analysis: Dispersion of Sentiment

It was found that the sentiment analysis suggests that 59% (1,753,650) of original tweets were negative, 23% (683,626) positive and 18% (535,012) neutral. The tweets classified as negative tended to condemn the violent actions by both sides while those classified as positive tweets were shown to include messages of peace. Examples of these tweets are shown in Table 8.

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Table 8 Examples of positive and negative tweets

As depicted in Figure 3, tweets containing certain popular hashtags such as #Palestine, #StayHuman and #GiveBackOurFreedom were classified as more positive than other hashtags. These tweets tended to include messages looking for peace between both sides. There was no significant difference shown between the sentiment expressed based on gender or language.

Figure 3 Sentiment Analysis: Sentiment by Hashtag

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The antagonists:

The asymmetry between the antagonists persists in Twitter as well, given that on July 19th the official Al Qassam Twitter account was suspended. Twitter did not offer any explanation for the suspension, but Hamas is listed as a terrorist organization in the US, and Twitter is a US-based corporation. Al Qassam however continued tweeting through other accounts, in English, Arabic and Hebrew, but its tweets did not gain very significant traction.

The contents of these accounts were coded and analysed in terms of the type of communication they tweeted. This analysis selectedthe tweets between the 19th July, when the formal Al Qassam account was suspended, and 26th August, to allow for a direct comparison of the tweeting activity of the antagonists. Given that Al Qassam operated multiple accounts we selected the top twoAl Qassam accounts in English in terms of followers. Table 9 presents the findings. It is clear that while text was most popular among both antagonists, IDF also tweeted pictures, infographics and videos to a much greater extent. This as we willsee in the analysis of retweets is significant as some types of tweets are retweeted more than others.

Type of Tweet@IDFSpokesperson

@Qassam_English

@qassamsms

Total

BLOG 65 8 62 135INFOGRAPH 103 2 12 117LINK 5 0 12 17PICTURE 80 31 120 231TEXT 352 426 230 1008VIDEO 115 8 14 137Grand Total 720 475 450 1645

Table 9. Types of tweets of the antagonists

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Retweets

The aim of this section is to establish the resonance of tweets discussed above and their impact on the broader twitter network. Analysing retweets provides an explicit approach to the diffusionof information across Twitter (Stieglitz and Dang-Wuan, 2013). Previous studies indicate that the quantity and speed of retweeting is a good measure for message virality and a screen-name’s influence (Hoang and Lim, 2011; Cha et al, 2010). We also analyse the screen-names who are retweeting these messages. This gives us an understanding of people who were communicating and the geographical hubs of support.

There were 46,233,100 retweets during the period of this study. The analysis of this section is based on a sample of 462,331 retweets, which is statistically significant to a confidence level of 99.99%. This section of analysis aims to address who arethe influential screen-names in the dataset, what is the content of the information being shared and what is the profile of screen-names retweeting. Based on the sample it was found that the top five retweeted screen-names remained consistent across the period of study

Table 10 Most retweeted screen-names

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The hashtags and text content of the retweets mirrors the findings of the original tweets. The dominant hashtags contained in the retweets were #gaza, #gazaunderattack, #palestine, #Israel, #prayforgaza, #freepalestine. An analysis of the text shows that a large proportion of the retweets contain informationrelated to rocket attacks on Gaza. The most used keywords included children, killing, rockets, support, killed support, dead, pray, hospital, school, civilians, family and protest. A word and hashtag summary of tweets is shown in Figure 4.

Figure 4 A Word Cloud

URLs also featured in a large volume of retweets. Examples of three of the most retweeted URLs can be found in Figure 5.Figure 5 illustrates one of the most retweeted tweets which depicts a

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child firing a rocket launcher on a beach. News articles also featured highly in the links retweeted, while the third example shows Israel’s use of Twitter for fundraising.

Figure 5: Examples of highly re-tweeted tweets

To gain an understanding of the screen-names who were retweeting during the conflict, we undertook an analysis of the screen-name descriptions (bios). The predominant word used in their bios was ‘Love’. Many of the bios included reference to religion and politics; keywords included politics, political, activist, allah,muslim, conservative. Other notable keywords were family, peace, journalist, student, writer, human, life, endorsement, yang and university.

The location of screen-names retweeting was consistent with the findings of the original tweets. The United States was the top location of screen-names who retweeted, followed by the United Kingdom. Details are show in Figure 6.

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Figure 6: Location of the retweet screen-names

The antagonists

The asymmetry observed earlier is repeated here as well. Communications emerging from IDF were much more likely to be retweeted than those coming from Al Qassam. Figure 7 shows the retweets received by each of the accounts. Figure 8 shows the topten retweets by type. All of the most retweeted tweets were from IDF and they were infographics, a combination of text, information and graphics.

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Figure 8 Retweets by Type – the dominance of IDF Infographics

Discussion

This section discusses the findings in terms of the two theoretical constructs of mediality and vectors.

Mediality

As mentioned earlier, mediality is understood as the specificity of Twitter as a communication medium, the noise it produces or adds to the communication. Here, we have operationalized this as including the varieties of actors involved, the specific hubs andthe contents of the communications.

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The varieties of actors shows firstly significant differences compared to the first phase of mediatization, which was dominatedby the news media and governments/antagonists. The main findings include that there is a broad base of a large number of users, and that activist accounts supersede both the news media and the two antagonists (IDF and Al Qassam/Hamas) in terms of volume of communication. This points to the increased importance of Twitterfor activists but also for witnesses, people who were caught up in the conflict. These witnesses are not necessarily political activists, but they are invariably pro-Palestinian. More broadly,activist accounts are divided between pro and anti-Israeli, and the top two accounts (see Table 2) which together produced over 20,000 tweets in the period of the six weeks of the war, were pro-Palestinian. This kind of intense activity is aimed to redress the asymmetry and bias in the coverage of the war by Western mainstream media, as evidenced in Philo and Berry’s work.however, if we turn to look at influence, it is significant to not that mainstream media and antagonists/politicians still enjoyhigh levels of influence and visibility, as evidenced in the mostmentioned (Table 4) and most linked-to domains (Table 6). It is noteworthy that although almost 7,000 users identified themselvesas journalists, only five made it in the most-mentioned list (@seanhannity, @mogaza, @maxblumenthal, @jonsnowch4, and @jncatron). Of these, only @mogaza was actually a local journalist. The information flows came from activists, witnesses,IDF and big media, with the general public retweeting and commenting on this information. Notwithstanding the critical reception of mainstream media coverage, as evidenced in the protests against the BBC in the UK, the same mainstream media were one of the main actors on Twitter.

Concerning the antagonists themselves, it transpired that there was a clear asymmetry in their influence and role in communicating during the war. IDF’s tweets were more in volume,

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had more resonance and were more influential. Al Qassam/Hamas wasnot able to contribute much to the debate. An interesting finding concerns the existence of retweet bots, which automatically produce communications.. Finally, Twitter itself must also be understood as an actor, since it suspended accounts and removed contents.

The overall analysis of this dataset shows that the most influential actors were news organisations and political leaders.However, a new category of infunetial actors are those witnessingthe conflict. and sharing their experiences. For example, @Farah_Gazan, a 16 year old girl from Gaza, who shared her experiences using the hashtag #GazaUnderAttack gained 70,000 followers in three days and became the 13th most mention screen-name during this period. It is suggested that highly influential individuals will be mentioned more in tweets (Bae and Lee, 2012);however, here we see an indication of a possible shift in influence during conflicts to individuals sharing their experiences as they live them.

This is therefore one of our main findings: the role of pro-Palestinian accounts by activists and by Palestinians caught up in the war has proven to be both significant in terms of the volume of communications they were putting out and influential interms of its resonance. Secondly, we identified evidence of the emergence of social bots as a propaganda strategy from both sides. The bot analysis undertaken suggests different bot tacticsinclude news aggregation, human user mimicking and automated retweets. One might posit that in addition to the antagonists and Twitter, there is evidence of the computer as a social actor in the dataset and this is worthy of further research. The new hubs that emerged on Twitter during this period therefore included a variety of actors, located both in Gaza and elsewhere,but also activists seeking to manipulate the discourse through the use of automated software bots. Notwithstanding the evidence

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of bots, we posit that the greater volume of real users suggests that none of the antagonists or indeed anyone else could control or steer communication, although they contributed to it and in some instances attempted to manipulate it.

In terms of contents, it has clearly emerged that the majority oftweets and retweets were from individual activist screen-names providing information on the conflict. This is consistent with the findings of Bagarajan et al. (2010) who noted that tweets categorised as sharing information generated a high volume of retweets. Despite efforts from the Israel Defense Forces, it is clear from the content analysis of tweets and retweets, that the majority of the screen-names are discussing attacks by Israel andshowing solidarity with Gaza. Links to other social network and news websites also featured highly in the retweets.

At the same time, a significant finding here concerns the ad hoc character of these communications. This was evident in the volumeof communications from some accounts and in the finding that someof the original contents had been removed. For example, the screen-names Mirko_48_67 and Mirko_NT that produced an astonishing number of tweets during this period have reverted back to a few, if any tweets per day in the post-war period. The removal of contents was quite common in this dataset, although itwas not clear whether this was undertaken by the users or the platforms. While it is understandable that a crisis event such asthis would generate more communication, the ad hoc character of the new hubs points to a loss of momentum for Gaza and Palestine.

The mediality of Twitter in this instance can be summarized as allowing a many-to-many kind of communication, albeit one that has a pyramid structure and hence a clear hierarchy in terms of volume, popularity and diffusion of contents. The two antagonists’ positions were very different, with IDF clearly having more influence than Al Qassam, but neither were as popular

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as some of the activists’ accounts, nor were they involved in disseminating high volumes of information. Twitter has also amplified the voices of Palestinian civilians caught up in the conflict, who can now communicate events directly as they witnessthem. Further, the noise generated on Twitter is ad hoc: it roughly lasted for as long as the war. Any gains for the Palestinian cause must therefore be seen as ad hoc. This raises questions regarding the ability of Twitter –based communication to introduce longer term shifts in the global public opinion. Finally, although the volume of tweets was astonishing, almost 50million if we take both original and retweets into account, it was still a very marginal part of the Twitter universe, where a single day sees about 500 million tweetsiii . Vectors

The notion of vectors is useful in setting the outer parameters of the communications on Twitter. We can understand these as ‘lines of flight’, Deleuze and Guattari’s (1980) term for understanding the point at which something turns into something else. We are therefore looking at the points that circumscribe Twitter’s communication on the war in Gaza in geopolitical, ideological, and affective terms.

In geopolitical terms, the three main points were located in the US/UK, in Indonesia, in Israel and in Venezuela. This was a trulyglobal communication environment. Given the geostrategic alliancebetween the US and Israel, it is expected that this point corresponds to the US position on the conflict, that is, unequivocal support for Israel. Indonesia, secondly, is the world’s most-populous Muslim majority country, and we can therefore assume that this point corresponds to solidarity shown to Gazans and also to Hamas from a Muslim-religious point of view. Finally, the ‘Bolivarian’ revolution of Venezuela suggests a third point that corresponds to support for Palestinian self-

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determination, independence and de-colonization. These vectors should be taken to correspond to the outer limits of the communications rather than to clear ideological positions. Nevertheless, they provide a set of geopolitical parameters within which to understand Twitter communications on the war.

Secondly, in ideological terms, looking at the actual contents ofthe communications, we can observe three main vectors: (a) clear support for Gaza and Hamas, (b) similarly, clear support for Israel, and (c) a humanitarian position. While the first two present the poles of the communications on Gaza, the third vectorpresents a line cutting across both of these, prioritizing the human costs of war, the loss of lives and the destruction and havoc caused. Hashtags such as #stayhuman correspond to this, while hashtags such as #icc4israel (international criminal court for Israel) combine a pro-Gaza with a humanitarian perspective. On the other hand, tweets such as this from @IDFspokeperson combine the humanitarian vector with the pro-Israel one: ‘Hamas forces Palestinian civilians to suffer. Hamas is responsible for the humanitarian situation in Gaza.[…]”Finally, the three main affective vectors were (a) sadness, (b) anger (c) pride. The vector of sadness and sorrow corresponds to communications that touch upon the loss that war brings with it. The most common words in the dataset point to the importance of this vector: children, killing, support, killed, dead, pray, hospital, school, civilians, family all point to loss and sorrow.The second vector speaks to the anger generated by this destruction and loss and often leads to communications associatedwith some kind of retribution or protest, for example, ‘#Hamas pledges to dump #Israeli army in mud of #Gaza in response to brutal crimes of Israel’. Tweets calling for peace are motivatedby both anger and sorrow. The vector of pride points to tweets associated with actions in which the antagonists or they supporters take pride, as for example: IDF Chief of Staff: ‘There

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are no militaries like ours. There are no militaries that drop leaflets and telephone civilians before a strike.’; ‘Al-Qassam Brigades had kidnapped Zionist soldier "Aaron Shaul" of military number 6092065 through heroic operation’; ‘15000 ppl of all religions, creed and colour unite to freePalestine. Simply awesome!!!’.

The large volume of communications in the dataset prevents a thorough analysis of contents. The concept of vectors helps identify the broad parameters of the communications, the points which circumscribe the debate. While overall the analysis points to a significant proportion of the communications supporting Palestine, the communications were more complex, point to differences in geopolitical positions, in ideologies, and in affect.

Conclusions

This article selected and analysed all the tweets that were posted in the period of the Operation Protective Edge. The large dataset showed some significant departures from previous communications of this conflict. In theoretical terms, we understood these findings as pointing to the mediality of Twitter, and the vectors of communications on Gaza. In terms of Twitter’s mediality, we found in this instance to have a pyramid structure.. The top of the pyramid included the ‘usual suspects’,i.e. politicians, journalists, and military spokespersons, but also some unexpected ones. One of the main findings here concernsthe rise of a new category of communicators, the witnesses, who are experiencing and communicating the war as it happens. They co-exist alongside more traditional communicators, including, governments, militaries, activists and media. The volume of

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Twitter’s communication on Gaza was very large, almost 50 million, but again a pyramid structure emerged here as only a small proportion of these (6%) were original. In terms of influence, the most mentioned accounts were predictably those of politicians and media. The vectors we identified were geo-political, substantive/ideological and affective. Specifically, the geopolitical parameters of the communications included not only the US/West, but also Indonesia, corresponding to Muslim solidarity to Palestine, and Venezuela, corresponding to solidarity from a de-colonization and self-determination perspective. In terms of the substantive contents, the parameter of humanitarianism, focusing on the human cost of the war, inserted itself into the polarized pro and anti-Palestinian communications. Finally, the affect of pride was observed along with the affects of sorrow and anger.

The question of the longer term impact of these communications onthe conflict in Gaza remains to be seen. In the short term, and notwithstanding the overall solidarity and support to Gaza shown on Twitter, nothing much seems to have changed in Gaza which remains under blockade and in ruins. The ad hoc character of the communications points to a loss of momentum for Palestine and Gaza. While in the throes of war, the gaze of Twitter was turned to Gaza, now it seems all but forgotten. Before any conclusions on the overall positive outcome for the Palestinian case, the ephemerality of these communications must be taken into account.

Theoretically, this paper supported the notion of diffused war, as developed by Hoskins and O’ Loughlin (2010). With respect to atransition from diffused to arrested war, in this paper we found synergies between mainstream media, internet platforms and user contents but on Twitter there was no dominance of a single user category. However, although there is no single source of steeringand controlling communications and while uncertainty dominates,

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we found here a clear pyramid structure both in terms of the actors/communicators and in terms of the contents and their diffusion. This suggests that diffused war does not necessarily lead to a collapse of communication hierarchies, but may create new or modify existing ones. Additionally, the notion of mediality allows us to trace the various actors/communicators on the specific media platform, but also the media platform itself as an actor/communicator: Twitter has written itself into the communications both in terms of mediating them and in terms of erasing/removing them. Additionally, we documented the rise of bots as actors attempting to influence Twitter communication. Finally, the notion of vectors allowed us to trace the outer points of the communications, in geographical and political, ideological/substantive, and in affective terms – communications travelled from one point to the next but without being defined bya single one. Future research may seek to refine these theoretical constructs in ways that allow a more nuanced understanding of their limits and contributions.

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Endnotes

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i Although there is an ongoing debate regarding the notions of mediation and mediatization (Couldry, 2008; Livingstone, 2009) in thecurrent article we are following Hoskins and O’ Loughlin’s (2010) terminology. ii Twitter data can be gathered through the Twitter API or through data aggregators e.g. Datasift, GNIP and Crimson Hexigon. The TwitterAPI limits access to 1% of tweets; data aggregators provide 100% of tweets through the Twitter firehose. iii Source: http://www.internetlivestats.com/twitter-statistics/