The Hashtags Rivalry behind the Controversial Bill : A comparative study on the Opposition and Support Movement of Omnibus Law Bill in Indonesia.

• Main Author: Damayanti, Imelda
• Format: Others
• Language: English
• Published: Linnéuniversitetet, Institutionen för samhällsstudier (SS) 2021
• Subjects: Connective Action; Participatory Propaganda; Social Network Analysis; Topic Modeling; Omnibus Law; Indonesia; Social Sciences; Samhällsvetenskap
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-104712

A controversial bill aimed to stimulate investment and boost the economy in Indonesia, called the Omnibus Law Bill, is followed by both protest and support expressed in social media prior to its signatories in October 2020. During that time, the Twittersphere is packed with both the Opposition and Support movement of the bill, who both benefit from the use of hashtags. To distinguish an organic grass-roots movement from a propaganda that fits the agenda of the government and elite, a comparison study is conducted with a framework of top-down and bottom-up- mechanism of information virality (Nahon & Hemsley, 2013). The top-down mechanism combined with participatory propaganda theory is designated to explain the Support movement. Vice versa the bottom-up mechanism is combined with connective action theory designed to explain the Opposition movement as its character in line with a contemporary and digital protest movement (Bennett & Segerberg, 2012). As existing research only often studies both networks alone, this unique case provides an opportunity to compare both networks. A mixed-method of Social Network Analysis (SNA) and Topic Modelling used to differentiate the characteristics of both groups, based on both network structure and topics discussed. The finding in regards to the SNA is corresponding to the theoretical framework and previous studies. The loosely organized nature of connective action is reflected in several characteristics of the Opposition Network, in contrast to the element of coordination found in the Support Network. Findings from bi-term topic modeling, however, both contradict and support the hypothesis that suggests more variations in the topics within the Opposition Network as a result of the self-motivated participant and personalized messages (Leong et al., 2019).