How #PrayFor memes spread

By Drew MargolinCornell University

The Bastille Day terror attack in Nice, France, brought the usual outpouring of emotion and expressions of support through social media. Front and center is the use of the #PrayForNice hashtag.

While this hashtag began years ago, it is becoming increasingly scrutinized as a social and political act. For example, after the Nice attacks, a number of tweets expressed frustration with the relative outpouring of sympathy for Nice compared with other cities that had been targeted by violent attacks, notably those in the Middle East and Africa.

What could lead to this accusation of indifference, and what should we conclude from it?

#PrayFor is the norm

A couple of years ago, my colleagues and I showed new phenomena that capture a large amount of public attention often draw many competing hashtags before winnowing to just a few over time. This appears to have happened with #PrayFor as a response to tragedy.

Since its emergence with the #PrayForJapan hashtag after the Tohoku earthquake in 2011, the use of #PrayFor hashtags to respond to terrible events has gone from a common response to a conventional one (e.g., #PrayForLondon, #PrayForBoston, #PrayForDallas).

Research suggests that hashtag use is governed by “complex contagion” dynamics, in which people wait to see if multiple friends have adopted a hashtag before adopting it themselves. This convergence can emerge naturally from the desire to meet others' expectations. The more people see sympathy expressed with #PrayFor, the more they expect #PrayFor is where others will look for sympathy. And so the more they direct their messages to it.

Now that #PrayFor has “self-organized” into a convention, people treat it more like a reflection of a single, collective voice, rather than as an amalgamation of individual behaviours. For example, some express frustration and despair regarding its perfunctory nature, as though it were a kind of policy that was sadly ineffective.

Others have decried #PrayFor’s religious overtones, concerned that the expression is substituting for real action.

Recently a new line of criticism has formed: the gap between how frequently #PrayFor hashtags are used for some tragedies, mostly in the West, compared to others, mostly in the Middle East or Africa. Even before the Nice attacks, The New York Times ran a story quoting a tweet which said “Why isn’t #PrayForIraq trending? Oh yeah no one cares about us.”

But does the fact that it is now conventional or “normal” to send #PrayFor tweets mean that this perceived difference reveals bias or insensitivity?

Why the difference in who gets #prayfor'ed?

Because #prayfor is now the norm, it is difficult to suggest differences in its use are due to a lack of awareness. Perhaps four years ago someone could say “I didn’t know there was a #PrayForMedina tag,” but no one would buy this now. Using #prayfor has become the norm in social media, and so failure to follow it in certain cases implies active discrimination.

But what accounts for this discrimination? The simple conclusion is that it is based on social categories like race, religion or civilizations.

But our research using geo-tagged tweets suggests that isn’t so. We found the use of #PrayFor is determined by the extent of personal social connections between the city where the attack takes place and the cities where the people sending the tweets live.

We measured personal connections in two ways. First, we examined the number of geo-tagged tweets sent from people who tweeted from both Boston and a given city in the two weeks prior to the attacks there in 2013. So if someone had visited Boston from New York in the previous two weeks, and had tweeted from both places, this counted as a personal connection between the cities. Similarly, if a Bostonian had visited New York in the last two weeks, and had tweeted from both places, this counted. We then compared the intensity of these connections to the total volume of geo-tagged tweets sent from the city. This tells us the extent to which (geo-tagging) Twitter users tend to go back and forth between the cities. We also measured the number of reply tweets sent between individuals located in Boston and the focal city.

In our analysis, we found cities that tend to contribute the most #PrayFor tweets are those that “exchange people” with the city that was attacked. For example, many tweeters had traveled between Boston and Los Angeles. After the bombings at the Boston Marathon, people from Los Angeles were more likely to tweet #PrayForBoston than people from Indianapolis or Chicago, which had fewer personal exchanges with Boston.

This pattern applies internationally, too. For example, Jakarta, which had a substantial number of #PrayForBoston tweets for a city that so far from Boston, also had exchanged more tweeting visitors with Boston than other Asian cities.

We see a similar pattern for the number of Twitter replies – the more Twitter replies between cities before the attacks, the more #PrayFor tweets after the attacks.

Room for exchanges

So we should expect that if a lot of Americans tend to go to Nice, then a lot of Americans will tweet #PrayForNice. The fact that there were Americans who were killed in the attack in Nice suggests personal exchange between Nice and U.S. cities is in fact substantial. Similarly, if few Americans go to Medina, and few Medinans come to America, few will tweet #PrayForMedina.

Of course, this explanation doesn’t change the fact that those in Medina would feel less supported than those in Nice, and it shouldn’t serve as a blanket excuse for apparent Western indifference. However, there is an important difference between a categorical denial of sympathy – not feeling bad for the suffering of all of those who fit a social label (e.g., Muslim, Arab) – and one based more on personal experience.

In particular, it suggests that differences in who gets “#PrayFor'ed” may be based on differences in the activation of small, specific groups rather than a large majority. That is, there are enough people using social media with specific, personal connections to the site of the attack to generate a substantial flow of #PrayFor tweets. This process is likely amplified by complex contagion, as connections between those who are directly motivated then spreads the use of #PrayFor to their friends.

By contrast, when a site in the Middle East is attacked, there are fewer people in other parts of the world with a direct connection to get the contagion ball rolling. The result is dramatically fewer tweets.

Furthermore, because this difference is based on specific social connectedness, rather than broad membership in categories, we can actually do something to change it. We can encourage people to travel to new places and encourage them to make friends with people from other social groups. By doing so we implicitly encourage them to be more sympathetic to otherwise “other” places.

It is much harder to change the big, global categories into which we place people. If the source of the disparity is personal, this gives us reason to #hope.

The Conversation

Drew Margolin, Assistant Professor of Communication, Cornell University

This article was originally published on The Conversation. Read the original article.

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