https://digiday.com/media/twitter-ipo-data/

 

Research Article I: #RefugeesWelcome

Summary

This research examines the utilization of digital movements of opinion (DMOs), specifically their role in digital participation.  The research also includes the application of DMOs to a better understanding of digital citizenship participation.  The overarching goal of the research is to provide an outline for DMOs in order to contribute to a lack of conceptualization of DMOs.  The research uses the Twitter hashtag- #RefugeesWelcome as their digital data source- employing several digital data methodologies including triangulation of network and content and metadata analysis- according to Barisione, Michailidou, and Airoldi, 2019 “user’s metadata analyses, network mapping, and qualitative content analysis.   The researchers argue that a better understanding of DMOs through digital data analysis will provide insight into how digital spaces are used as a mode of political force.

Use of the Data

This research included the multiple uses- one was the use of a social media analyzation tool to track Tweets as well as Python programming for producing network analysis and visualization of Twitter networks.  A data set of over 1 million tweets along with the use of visual  output functions for both the distribution of Tweets as well as networks from #RefugeesWelcome was effective in providing support to the goal of the research.  The distribution of Tweets graphed using a line graph illustrates trends in Tweets over time and the various network networks models shows the dynamic relationships that occur on #RefugeesWelcome.  Although the research uses various digital analysis methodologies with a data set of over 1 million tweets to illustrate trends over many months as well as network outputs, the research still utilizes an inductive approach to understanding DMOs.  The specific data pulled from #RefugeesWelcome over the months provides specific insight that can help researchers determine what may be taking place at more general levels.

Strengths and Weaknesses

My critique of this research is as follows:

Strengths- Effective use of tools for extracting and examining Twitter data- extracting over 1 million Tweets provides a robust data set, and use of graphing and network visualization provides support for the research purpose.  The data set was large enough for better understanding the use of DMOs (over 1 million Tweets). The use of multiple analysis methods provides a more comprehensive examination of the data which reduces the risk of error and misinterpretation.

Weaknesses- The research only examines one specific episode of DMOs and then only across less than a year of data.  This leaves several considerations- do those on Twitter that are a part of DMOs respond differently to various situations- so what are the differences in how a user may respond to various social phenomena- in this case it was the attacks in Paris, may it be different for an occurrence less violent and impactful?  Also, there are other data analysis tools that could be used such as sentiment analysis in order to examine the overall mood of those responding on #RefugeesWelcome and determine if there is a trend in mood and resulting actions that take place- more of a predictive tool.

Research Article II: Riots and Twitter

Summary

This research examines the phenomena of collective action resulting from social media spaces, specifically how Twitter can be utilized to create opportunities for collective action.  The broader focus is to provide support to be able to respond to criticisms of collective action theory.  The methodology used in order to support this research included an analysis of software systems as well as issue publics and discourse for analysis of connective politics applied to in this research case the UK 2011 riots (Pond, Lewis, 2019).  The overarching goal of the research is to understand how collective action occurs in digital spaces and the dynamics that shape how collective action originates and its outcomes applied to specific social events.  The research examines the Twitter hashtags- #RiotCleanUp and as a comparison uses #OperationCupOfTea.  This research argues that there is an empirical measure available for comparing discourses related to the influences upon collective action.  The research through various forms of data analysis finds support for this argument and shows how dynamic collective action in digital spaces can be- with emphasis on the complicated nature of digital networks and relationships within them.

Use of the Data

The data analysis in this research utilized an empirical methodology in order to examine discourse within and between Twitter hashtags.  This approach included three elements- an overview of discourse, differentiation between hashtags, and finally the interpretation of influence from the first two elements.  The digital analysis was conducted using a script to extract hashtags from Twitter.  There were seven hashtags involved in data extraction.  Random sample of tweets were taken  in order to analyze discourse between various hashtags and additional coding was used by two researchers focusing on typology and theme based understanding of the tweets.  This research takes an inductive approach, taking a specific event and using a specific reaction on Twitter in order to understand better a general occurrence- collective action.

Strengths and Weaknesses

My critique of this research is as follows:

Strengths-

Strengths- Effective use of tools for extracting and examining Twitter data- but with limited information on how the extraction was taking place limits my comment about the coding itself.  The use of seven different hashtags was effective in producing results across more than just a few hashtags- this should be done across other research from the Twitter environment.  The use of multiple hashtags allows researchers to find “like” topics and expand their data source and number of samples.  Visualization of data using bar charts was effective in representing the frequency of different codes associated with the data as well as the effective use of the line graph for comparing #London Riots to #UKRiots.  In my opinion this research used sound methodologies and effective forms of data analysis.

Weaknesses- The random sample of 1000 tweets could have been increased to produce a more comprehensive examination of the data from the two researchers running concurrent analysis.  There also could have been a network analysis conducted in order to examine the relationships between Twitter users.  There could have also been a sentiment analysis done in order to better gauge the overall mood of users who responded to the various hashtags.

 

Cool video regarding Twitter Data (check out the mapping used from the Tweets regarding who says Hello).

 

Reference(s):

Mauro Barisione, Asimina Michailidou & Massimo Airoldi (2019) Understanding a digital movement of opinion: the case of #RefugeesWelcome, Information, Communication & Society, 22:8, 1145-1164, DOI: 10.1080/1369118X.2017.1410204

Pond, Philip, and Jeff Lewis. “Riots and Twitter: Connective Politics, Social Media and Framing Discourses in the Digital Public Sphere.” Information, Communication & Society 22.2 (2019): 213-31. Web.

TED-Ed. (Feb, 2013). Visualizing the world’s twitter data- jer thorp. Retrieved from https://www.youtube.com/watch?v=tI61JjXdo_I

 

 

 

 

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