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Was opposition to Aurat March real or manufactured?

SAMAA | and - Posted: Mar 12, 2020 | Last Updated: 4 months ago
Posted: Mar 12, 2020 | Last Updated: 4 months ago
Was opposition to Aurat March real or manufactured?

Photo: Online

This year’s Aurat March has stirred an avalanche of reactions and over reactions, arguments and counterarguments, some friendly and some hostile.

Social media serves as the most ideal example of this. As #AuratMarch became a leading trend on Twitter, so did a number of opposing hashtags. The controversial slogan “Mera Jism Meri Marzi” and its associated hashtags went viral, sparking clashes both online and offline as everyone from human rights activists to polemicists to provocative playwrights expressed their two cents on the issue.

Many people feel that the controversy surrounding the march undermined the overall objective of the event, which was to raise awareness of women’s rights. But a critical question emerges: was this opposition real? Or was this conflict manufactured to artificially generate controversy?

We sought to analyze the social media behaviour of key trends in opposition to the Aurat March. Using open-sourced software, five key hashtags were identified, and their behaviour examined on the following criteria:

Identify the major opposing trends
Based on software analysis, a number of hashtags cropped up in response to the Aurat March. The ones that stood out were:
#فحاشی_مارچ_نامنظور
#HayaMarch
#ایکسپائر_کھسرے
#WeRejectMeraJismMeriMarzi
#AmirLiaqat_Laanti

Analyse what type of content was generated
Based on each hashtag, we were able to isolate the tweet type or edge – whether users were tweeting, or retweeting, or mentioning or replying, etc. We calculated their proportion in the overall discussion. Since there was a large number of users and tweets, we have rounded them off to the nearest hundred (e.g. 6,032 users has been represented as 6,000) for ease of understanding.

Examining connections between users
Based on statistical modeling, we are able to visually represent the behaviours of the top users, showing:
Impact of individual users (through size of circles)
How diverse the user pattern and content was (through diversity of colors)
Coordination between users on content (through similarity of colors)
Connections between users on content (through lines connecting the circles)

The diagram below shows this representation, which we will employ further in this analysis:

#فحاشی_مارچ_نامنظور


Roughly translated as “Vulgar March Unacceptable”, this trend gained a lot of traction leading up to Aurat March and even while it was ongoing. A mixture of religious and provocative posts was generated on this hashtag. A sample of 6,300 users and 18,200 tweets on this trend was analyzed.

The breakdown shows that the vast majority of the content was retweets, with little original content. This indicates inorganic trending by a dedicated group of users. Furthermore, this was not being done by large numbers of users, but rather two groups affiliated with Tehreek-e-Labaik Pakistan that were working with each other on propagating this content.

This is made even more clear by the diagram above. The top users sent a high volume of tweets (minimum of 100, and maximum of 350), which is an unusually number of activity by a relatively small number of users. The diagram shows strong connections between the users, as reflected in the large number of green and pink circles and the lines connecting them. The lack of diversity in the colors points to the fact that specific users were dedicated to this hashtag.

Conclusion: The trend was inorganically propagated by two groups working in close coordination with each other.

#ایکسپائر_کھسرے


This hashtag roughly translates into “expired eunuchs.” It trended with anti-Aurat March memes.

We managed to isolate approximately 6,000 users with around 29,000 tweets here.

It becomes clear in the data set that 88% of the tweets were mainly retweets and not organic content.

The above diagram shows the top 30 tweeters. Each of the following users tweeted/retweeted at least 200 times. The large number of blue circles shows that 27 of these users were generating the majority of the content, with little outside engagement. This shows that they were working together to propagate the hashtag.

Conclusion: Not organic, propagated by a dedicated group of social media users who were working in close coordination.

#HayaMarch


A sample of 2,600 Twitter users with 3,400 tweets was analysed.

The retweet percentage is 69% that is very much in the range of organic trends.

The above diagram shows the connections between the top Twitter users carrying the hashtag. The minimum number of tweets/retweets among these users is 9, while 38 is the maximum number sent by a user, indicating relatively more organic activity. There is more diversity in the content generation as indicated by multiple colors, and the majority of content is not coming from a specific account or group of accounts. However, connections can be seen between some of the circles, showing independent, but supportive activity. This indicates that a number of like-minded groups joined in to populate the hashtag.

Conclusion: Activity was organic, but being propagated through smaller groups supported through crowdsourced efforts

#WeRejectMeraJismMeriMarzi


A sample of 19,300 users with 37,700 tweets was analysed for this hashtag.

The breakdown of tweets shows that nearly 4 out of 5 tweets were just retweets, which indicates inorganic content generation, or at the least inorganically supported. However, the large number of users shows that there definitely was a legitimate conversation between like-minded individuals with similar concerns.

The diagram above shows the top 30 users and their connections. The minimum tweets sent by a user in this group is 35, while the maximum is 226, showing a high amount of activity.

As seen in the diversity of colors, there were a number of voices contributing to the discussion on this trend. Furthermore, there is no particular pattern to the connections between the users, reflecting that while the hashtag was promoted by multiple individuals, they were not working as an organised group. The fact that over 19,300 users were actively part of the discourse shows the discussion was genuine and not fake.

Conclusion: While the trending of the hashtag may have been part of an organised effort as seen in the high retweeting, the conversation itself was organic, made successful with the genuine participation of thousands of users.

#AmirLiaqat_Laanti


This hashtag began trending shortly after a program on a TV network in which PTI leader and television personality Dr Aamir Liaquat criticized playwright Khalil ur Rehman Qamar for his personal and offensive remarks during a previous interview, and even suggested the writer needed psychological treatment.  

A sample of 440 Twitter users with 600 ties to this hashtag was collected and analyzed.

The breakdown above shows that the content was not skewed in favor of just retweets, but contained a high percentage of self-loop (tweeting and retweeting one’s own content repeatedly), replies and mentions. On the surface, this would appear to be a genuine conversation. However, the next diagram of top users shows quite the opposite.

As you can see, the bulk of the content is coming from just two users, represented by the two large circles. The content analysis shows that much of the material being generated on this hashtag was derogatory and offensive.

While the volume of tweets is not high, this hashtag became prominent as the users added other more popular hashtags while posting their content, essentially piggybacking on the success of other trends to make their own hashtag more prominent.

Conclusion: The trend was inorganic, driven primarily by two users, who exploited other hashtags to popularize their own.

Rizvan Saeed is a social science researcher. He tweets @saeedrizvan
Usman Zafar is a journalist and an academic based in Islamabad. He tweets @zafarsmu

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One Comment

  1. Irfan ashraf  March 13, 2020 2:13 pm/ Reply

    One sided biased analysis.

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