Hashtag Activism and Framing of a Social Movement

Marjan Davoodi

Contact: mxd1025@psu.edu

In July 2019, the House of Representatives passed HR1044 bill and introduced it to Senate. This bill was proposed to remove per country limitation for green card issuance. Later on, S386 bill was introduced to Senate that was very similar to the former bill, but added other types of employment-based visas to the proposition. The per country limits guaranties 7% of green card petitions for each country, out of the total 140,000 employment-based green cards allotted each year. The opponents of this limit believe that it is unfair for countries such as India and China with a large population. On the other hand, some are concerned that the removal of this limit would allocate green cards to Chinese and Indian population for many years in row and as a result, people from small countries would be in waiting status for up to 15 years. Since the initial introduction of these bills to the House and Senate, they have been amended multiple times and blocked by senators for various reasons. During this process, a hot debate regarding the given bills have been going on, mainly between people from India and China who support the aforementioned bills and the population from smaller countries such as Iran that oppose it. Both sides have recruited lobbyist, arranged protests, and created Twitter networks to invite people to make calls to the senators and persuade them to vote for or against these bills. The utilities of Twitter in diffusion of information regarding the forthcoming protests and boycott requests, as well as circumventing news blockage have not been futile. In this research, I investigate the role of social media in framing of social movements. My main research question is that if the new social media has the capacity to frame a movement and result in actual offline change. Therefore, I decided to address this topic as a case study of hashtag activism and analyze it with a social network approach. In the last couple of months, many relevant hashtags have gone viral by either of the beneficiaries, such as #S386, #HR1044, #NoS386, and #YesS386 to name a few. I created an edgelist for each type of interactions, meaning replies, mentions, and retweets. Since R is a powerful tool for data scrapping, it provided me a wide range of attributes, such as favorite count, retweet count, retweet’s retweet count, list of hashtags in each interaction, and number of friends and followers. To this point, I have created multiple networks with different arrangements, either for each interaction type separately or by merging them altogether. I have noticed that the wide range of edge and node attributes enables me to create networks with versatile edge values to test multiple hypothesis and respond my research questions. For instance, due to the evident clustering of opposing groups within this dataset, I aim to find out the central nodes who posit the bridging role and facilitate discussion between the two main groups with contradictory attitudes toward the bills.

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