Riots and Twitter: connective politics, social media and framing discourses in the digital public sphere
By: Phillip Pond and Jeff Lewis
This paper discusses how social media sites have enabled new forms of connective action through hashtags, memes and personalized action frames in political movements. It analyzes software systems, issue publics and discourse to give an account of connective politics during riot clean-up movements. It argues that networks assemble and mobilize through the activation of discourse within a wider media sphere of competing discourses. They get their data from Twitter by extracting hashtags from the “riot sample” using a cross-referencing process to determine the hashtags that define the dominant genres of topical discourse on Twitter during the period of interest. The researchers identified the five hashtags: #UKRiots, #LondonRiots, #RiotCleanUp, #OperationCupofTea and #Riots. This paper also argues that ambience must be a product of systemic interaction among software, users and text. The paper found that networked relationships – horizontal, widely distributed, weak-tie connections between social actors are but one component of a human-user system that is part of a large systematic, communicative assemblage. This paper also found that the hashtag #OperationCupofTea is an arbitrary signifier that reveals nothing about the people engaging in riot clean-up work, nor their motivations, meaning that connective action can only be understood through careful exploration and analysis of the discourses that created and propagated the action frames. This paper also found that it is not sufficient simply to describe discourse in the riot public, it is necessary to differentiate between discourses in a way that can explain connective influence.
The researchers use an empirical method to identify, analyze and compare three elements of hashtag specific discourse: 1) establishing an overview of discourse by identifying the dominant hashtags during the relevant period, 2) It must be able to differentiate between these hashtags in a way that supports a critical analysis of their relative influence and 3) it must reveal clues as to why some discourses energize group mobilization and others do not by providing a mechanism for interpreting this influence in terms of connective action. They used an inductive approach to their study by using different hashtags to find a correlation between them.
The strength is that they used data that was already online so it was easy and cheap to obtain. The weakness is that they used a limited number of tweets, they only used 1000 which is a lot but it limited the sample number.
Understanding a digital movement of opinion: the case of #RefugeesWelcome
By: Mauro Barisione, Asimina Michailidou and Massimo Airoldi
This paper analyzes the digital discussion around the Twitter hashtag #RefugeesWelcome as a case of “digital movement of opinion’ (DMO). It argues that the idea that citizen voice through social media can give rise, under given conditions, to a specific digital force combining properties of social movements and public opinion has received less attention. They also argue that the DMO concept is heuristically useful for the research on new forms of digital citizen participation, because it (1) provides an ideal-type allowing to study empirical cases by observing their adherence and deviations from the theoretical construct; (2) isolates the digital dimension of citizen participation, both as a methodological strategy and an epistemological posture; (3) bridges public opinion and social movement theories and thereby helps apprehend new/future forms. They use the hashtag #RefugeesWelcome on Twitter to collect their data. The researchers found that the hashtag #RefugeesWelcome gained momentum and rose to DMO status in the early stages of its life cycle and that it created a powerful digital voice that provided legitimacy to the refugee crisis and pro-refugee movement.
The researchers in this study used an inductive approach because they tried to determine the digital movement of opinion by analyzing the hashtags #RefugeesWelcome. They used three different methods: They used a triangulation of Twitter data, metadata and a qualitative analysis of text-based content.
The strength is that they used data that was already online so it was easy and cheap to obtain. The weakness is that they only analyzed one hashtag, which might have narrowed the findings too much. I feel like they should have analyzed more hashtags.