Kramer , argues that without the spread of secular values based on the ideas of human individuality and self-determination, a top-down imposition of secularism on society has not been able to guarantee individual freedoms and democracy; instead, it has alienated practicing Turkish Muslims by excluding not only political but also social expressions of Islam.
The AKP de-emphasizes Islam in order to embrace democracy, human rights and rule of law, values which originated in the West but are increasingly perceived as universal. Presented as a pro-European Union EU reformist movement within Turkish Islamism, the AKP has even been proposed as an antidote to the rise of religious fundamentalism and as a role model for the Islamic world Fuller However, since the EU reform process has slowed down in Turkey.
As regards wearing religious symbols, TESEV finds that in , approximately three out of eight Turkish women reported that they did not wear any head covering. Recent research led by Binnaz Toprak demonstrates that the secular segments of the Anatolian public feel threatened by the alliance between conservatism and religiosity. Both non-practicing Muslims and Alevites feel obliged to follow Sunni practices such as fasting during Ramadan. The intolerance towards youth is alarming. The report concluded that the activities of AKP municipalities have reinforced the conservative segments of Turkish society and increased societal pressure based on Muslim mores.
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The reaction of AKP -friendly media was to accuse Toprak of politicizing research. First, unlike other Islamic states in the world, the religious scholars, or ulema , are weak in Turkey. In Ottoman times, this was due to a complex bureaucratic structure that divided ulema powers among different bureaucracies. In contemporary Turkey, there are substantial limitations to ulema practices. For instance, considering Islam in political activities and law-making is constitutionally banned. Second, the Islamist intelligentsia in Turkey is predominantly influenced by a Sufi tradition that rejects radicalism and anti-state violence.
Third, Turkish Islam is local and nationalist in the sense that it does not resonate well with internationalist Islamist movements. Fourth, as it did not arise out of a previous colonization experience, it does not embrace anticolonialist discourses that reinforce a negative image of the West. Sixth, it is moderate and diffuse because significant segments of its elite and other followers have been secularized through education.
The indegree distributions, reciprocity levels, and degree correlation all suggest a hierarchical, core-periphery structure with a minority of participants at the core. For example, the maximum indegree number of times retweeted is ,, whereas the maximum outdegree number of retweets sent is 2, In addition to the network of RTs, we use the total number of messages containing any of the relevant hashtags for each collection regardless of whether they are RTs as node attribute information.
We also count, for each node, the total number of followers. These two attributes are the basis for our measurement of activity and reach. We measure reach as the fraction of followers that every participant has over the total number of followers in the network. As Fig 1 illustrates, this means that some followers like node l are counted more than once.
Because of the clustering in the Twitter network [ 23 ], our count probably overestimates the number of unique users who are exposed to protest information; however, prior research on complex contagion and the psychological effects of repeated exposure suggests that the adoption of a given behavior such participation in a protest is more likely after individuals become familiar with the stimuli [ 13 , 14 , 24 ].
Psychological research also shows that familiarity with statements increases the likelihood that those statements will be judged to be valid and true [ 25 , 26 ]. By counting the same followers more than once, we tap into the mobilization potential of repeated exposure—all else equal, node l will be more likely to join the flow of protest communication than other followers who are only exposed to protest information from a single source.
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For the same reason, we assume that a similar feedback mechanism exists among participants who are following other participants: the more information they are exposed to, the more engaged they are likely to become. Although we cannot disentangle any causal relationships given the observational nature of our data, this positive feedback is likely to drive at least in part the higher density of ties we identify at the core of the network, where participants are on average more active in posting messages and retweets.
In this schematic representation, there are three protest participants that accumulate six unique followers. The relative reach of each participiant nodes in red is the fraction of their direct followers over the total available in the system nodes in orange. We normalize these counts to fall in the interval [0,1] for the three networks.
Overall, our measure of reach is roughly comparable to more standard measures of audience share in media market studies e. We measure how many Twitter users are exposed to protest-related information through at least one of the users they decided to follow.
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This is similar to measuring the share of households with their televisions or radios on that are tuned to a particular channel. We cannot be certain that followers are reading the protest messages that appear in their feeds, much as rating measurements provide no guarantee that members of a household tuning into a particular program are actually paying attention. We identify core and peripheral participants using the k -core decomposition technique, which partitions a network in nested shells of connectivity [ 27 , 28 ].
The k -core of a graph is the maximal subgraph in which every vertex has at least degree k.
In our case, degree relates to the number of retweets made or received. The k -core decomposition is a recursive approach that progressively trims the least connected nodes in a network i. Fig 2 illustrates the k -core decomposition of a random graph with 19 vertices and 24 edges. Node degree is in the range of 1 to 5, but there are only four cores.
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Since the method is recursive, some of the nodes with degree 5 end up being classified in lower k -shells. Nodes classified in higher k -shells not only have higher degree: they are also connected to nodes that are central as well. Low engagement participants are classified in lower k -shells, and they form the periphery of the network. This technique recursively prunes the network to remove nodes with the lowest degree.
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We use the network of retweets to distinguish between core and peripheral participants. However, not all messages relevant in the dissemination of protest-related information are retweets to other users. This is clearly the case for individuals at the protests themselves, who are perfectly capable of producing original tweets with relevant information; this even holds for those not at the protests, who may write original tweets summarizing what they have learned from friends, colleagues, and other sources of new and traditional media.
Consider someone watching a Twitter feed and seeing five tweets in a row related to the use of teargas. We therefore measure protest activity as the total number of messages that contained at least one protest-related hashtag, regardless of whether that message was a retweet. For the non-protest-related network, we also measure activity as the total number of messages containing at least one hashtag or keyword related to the event. We produce three metrics: one at the level of individual participants, one at the level of k -cores, and one at the level of the entire network, which we use to assess the share that each participant and k -core contributes to overall activity volume.
Our analyses assess the impact of peripheral users by simulating how removing them from the network would affect the two outcome variables, audience and reach, in comparison to a random benchmark. This benchmark is based on a random assignment of k -core values sampled without replacement, averaged over 10, permutations. The benchmark can, in fact, be interpreted as a line of perfect equality, that is, it plots what would happen if all k -cores contributed the same amount to overall activity and reach.
The farther the reach and activity curves fall from this line, the more unequally distributed these resources are in the network. Fig 3 illustrates the k -core decomposition of the communication network that emerged during the Turkish protests. The group with the highest percentage of users who reported being at the Taksim Gezi Park the geographical epicenter of the protests constitutes the core of the network, where most of the RTs are also directed—or sourced—from. This indicates that information flowed largely from the core to the periphery, allowing many participants who were not on the streets to be informed in real time of activity on the ground.
Access to timely information through online networks is especially important in the Turkish context, where mainstream media is heavily controlled and, in the early stages of the protests, was used to divert attention away from the events happening in Gezi Park.
Famously, the major news network broadcasted a penguin documentary rather than covering the massive protest mobilization and confrontation with police taking place in the park [ 29 ]. Participants have been grouped in their corresponding k -shells, here represented by nodes. Lower k- shells contain participants at the periphery of the network; higher k -shells contain core participants.
Node size is proportional to aggregated activity, measured as total number of protest messages not just retweets. Arcs indicate retweeting activity, and their width is proportional to normalized strength arcs with lower strength have been filtered to improve the visualization of the network.
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The darkness of nodes is proportional to the percentage of participants who reported being in the Taksim Gezi Park the geographical epicenter of the protests , as indicated by the geographic information attached to their tweets. Most of these participants are at the core of the network where most RTs are also sourced from, thus allowing information to flow from the core to the periphery.
Fig 4 shows that, on average, participants across all k -shells have a similar number of followers panel A. Panel C in Fig 2 shows how activity levels and overall reach would vary if outer k -cores were progressively removed. Thus despite the relative lack of reach of any particular individual in these last five k -cores, the reach of the active core participants would have been substantially diminished by the absence of these peripheral members of the network.
Panel A shows the distribution in number of followers or reach across k -shells. Panel B plots the distributions in number of tweets sent or activity across k-shells. Panel C shows the effects on overall reach and activity of removing k -cores progressively, starting from the lowest or most peripheral as illustrated by the networks below the horizontal axis. The random benchmark is based on 10, permutations of the data where assignment to k -cores is randomly re-shuffled; this benchmark can be interpreted as a line of perfect equality, i.
We replicated the same analyses with two more datasets related to protest events: one tracking communication related to the Occupy Wall Street movement, the other tracking the Spanish Indignados, both around the same global call for action in May of Panels A and E in Fig 5 illustrate the k -shells and their connectedness. Unlike the Turkish case, these protests did not have a clear epicenter, so nodes are colored in proportion to the number of retweets received or instrength [ 30 ], normalized to range between 0 and 1.
The networks are smaller and sparser than in the Turkish case see Table 1 , resulting in fewer k -cores; however, the core-periphery dynamics are similar: most of the information flows from the core to the periphery, where users are significantly less active on a per capita basis but who contribute as many messages at the aggregate level. Panels A and E visualize the connections across k -cores arcs with lower strength have been filtered to improve visualization.
Unlike the Turkish case, these protests did not have a clear epicenter, so nodes are colored in proportion to the number of retweets received i. Core-periphery dynamics are, however, similar to the Turkish case: most of the information flows from the core to the periphery, where users are significantly less active on a per capita basis but who, on the aggregate, contribute a similar volume of messages. The random benchmark is, again, based on 10, permutations of the data and it can be interpreted as a line of perfect equality. In contrast, Fig 6 reproduces the k -core analyses for the two networks that are not related to protest events.
The activity that comes from the core is significantly lower compared to the activity that arises from the periphery, which is disproportionately larger not only in terms of number of users but also in terms of aggregated number of messages. This is especially the case for the Oscars network, where the core is virtually invisible under the shadow of the periphery. What this means is that, as panel D shows, removing the first few cores has a higher impact on activity than on reach—exactly the opposite of what happens in the protest networks. The core is slightly more prominent in the minimum wage network, but again, removing the first few cores has a similar impact on activity and reach panel H.
These differences can be best measured as the area between curves, illustrated in Fig 7. Panel A shows the area between the activity and reach curves, and the random benchmark, which, again, can be interpreted as a line of equality where the contribution of all k -cores is perfectly equal. The areas are more similar for the two datasets that are not protest-related.
Furthermore, the only dataset that does not track political communication is the only one in which the area above activity is larger, signifying that the network is more hierarchical in the distribution of content creation with most of it concentrated in the periphery. Panel B plots the area between the activity and reach curves for the five datasets; this measure serves as an index of core-periphery activity.
Again, the non-protest networks show a visible difference: the two curves are closer, signaling a lack of division of labor between core and peripheral users. The diagrams on the upper-right corner indicate the areas that were computed in each panel. The Oscars dataset is the only one in which removing the lower cores has a greater impact on activity than reach—hence its different color in the barplot of panel B.