Social Media Data and the Dynamics of Thai Protests

2015 ◽  
Vol 43 (5) ◽  
pp. 545-566 ◽  
Benjamin Nyblade ◽  
Angela O’Mahony ◽  
Aim Sinpeng

Traditional techniques used to study political engagement—interviews, ethnographic research, surveys—rely on collection of data at a single or a few points in time and/or from a small sample of political actors. They lead to a tendency in the literature to focus on “snapshots” of political engagement (as in the analysis of a single survey) or draw from a very limited set of sources (as in most small group ethnographic work and interviewing). Studying political engagement through analysis of social media data allows scholars to better understand the political engagement of millions of people by examining individuals’ views on politics in their own voices. While social media analysis has important limitations, it provides the opportunity to see detailed “video” of political engagement over time that provides an important complement to traditional methods. We illustrate this point by drawing on social media data analysis of the protests and election in Thailand from October 2013 through February 2014.

Philip Habel ◽  
Yannis Theocharis

In the last decade, big data, and social media in particular, have seen increased popularity among citizens, organizations, politicians, and other elites—which in turn has created new and promising avenues for scholars studying long-standing questions of communication flows and influence. Studies of social media play a prominent role in our evolving understanding of the supply and demand sides of the political process, including the novel strategies adopted by elites to persuade and mobilize publics, as well as the ways in which citizens react, interact with elites and others, and utilize platforms to persuade audiences. While recognizing some challenges, this chapter speaks to the myriad of opportunities that social media data afford for evaluating questions of mobilization and persuasion, ultimately bringing us closer to a more complete understanding Lasswell’s (1948) famous maxim: “who, says what, in which channel, to whom, [and] with what effect.”

2019 ◽  
Vol 1 (2) ◽  
pp. 193-205
Ria Andryani ◽  
Edi Surya Negara ◽  
Dendi Triadi

The amount of production data generated by social media opportunities that can be exploited by various parties, both government and private sectors to produce the information. Social media data can be used to know the behavior and public perception of the phenomenon or a particular event. To obtain and analyze social media data needed depth knowledge of Internet technology, social media, databases, data structures, information theory, data mining, machine learning, until the data and information visualization techniques. In this research, social media analysis on a particular topic and the development of prototype devices software used as a tool of social media data retrieval or retrieval of data applications. Social Media Analytics (SMA) aims to make the process of analysis and synthesis of social media data to produce information can be used by those in need. SMA process is done in three stages, namely: Capture, Understand and Present. This research is exploratorily focused on understanding the technology that became the basis of social media using various techniques exist and is already used in the study of social media analytic previously.

2021 ◽  
Kashif Ali ◽  
Margaret Hamilton ◽  
Charles Thevathayan ◽  
Xiuzhen Zhang

Abstract Social media provides an infrastructure where users can share their data at an unprecedented speed without worrying about storage and processing. Social media data has grown exponentially and now there is major interest in extracting any useful information from the social media data to apply in various domains. Currently, there are various tools available to analyze the large amounts of social media data. However, these tools do not consider the diversity of the social media data, and treat social media as a uniform data source with similar features. Thus, these tools lack the flexibility to dynamically process and analyze the social media data according to its diverse features. In this paper, we develop a `Big Social Data as a Service' (BSDaaS) composition framework that extracts the data from various social media platforms, and transforms it into useful information. The framework provides a quality model to capture the dynamic features of social media data. In addition, our framework dynamically assesses the quality features of the social media data and composes appropriate services required for various information analyses. We present a social media based sentiment analysis system as a motivating scenario and conduct experiments using real-world datasets to show the efficiency of our approach.

2022 ◽  
Vol 22 (1) ◽  
pp. 1-25
Florian Meier ◽  
Alexander Bazo ◽  
David Elsweiler

A fundamental tenet of democracy is that political parties present policy alternatives, such that the public can participate in the decision-making process. Parties, however, strategically control public discussion by emphasising topics that they believe will highlight their strengths in voters’ minds. Political strategy has been studied for decades, mostly by manually annotating and analysing party statements, press coverage, or TV ads. Here we build on recent work in the areas of computational social science and eDemocracy, which studied these concepts computationally with social media. We operationalize issue engagement and related political science theories to measure and quantify politicians’ communication behavior using more than 366k Tweets posted by over 1,000 prominent German politicians in the 2017 election year. To this end, we first identify issues in posted Tweets by utilising a hashtag-based approach well known in the literature. This method allows several prominent issues featuring in the political debate on Twitter that year to be identified. We show that different political parties engage to a larger or lesser extent with these issues. The findings reveal differing social media strategies by parties located at different sides of the political left-right scale, in terms of which issues they engage with, how confrontational they are and how their strategies evolve in the lead-up to the election. Whereas previous work has analysed the general public’s use of Twitter or politicians’ communication in terms of cross-party polarisation, this is the first study of political science theories, relating to issue engagement, using politicians’ social media data.

2016 ◽  
Vol 23 (1) ◽  
pp. 7-17 ◽  
Andrea Ceron

Scholars have emphasized the need to deepen investigation of intraparty politics. Recent studies look at social media as a source of information on the ideological preferences of politicians and political actors. In this regard, the present article tests whether social media messages published by politicians are a suitable source of data. It applies quantitative text analysis to the public statements released by politicians on social media in order to measure intraparty heterogeneity and assess its effects. Three different applications to the Italian case are discussed. Indeed, the content of messages posted online is informative on the ideological preferences of politicians and proved to be useful to understand intraparty dynamics. Intraparty divergences measured through social media analysis explain: (a) a politician’s choice to endorse one or another party leader, (b) a politician’s likelihood to switch off from his or her parliamentary party group; and (c) a politician’s probability to be appointed as a minister.

10.2196/17087 ◽  
2020 ◽  
Vol 22 (7) ◽  
pp. e17087
Yulin Hswen ◽  
Amanda Zhang ◽  
Kara C Sewalk ◽  
Gaurav Tuli ◽  
John S Brownstein ◽  

Background Discrimination in the health care system contributes to worse health outcomes among lesbian, gay, bisexual, transgender, and queer (LGBTQ) patients. Objective The aim of this study is to examine disparities in patient experience among LGBTQ persons using social media data. Methods We collected patient experience data from Twitter from February 2013 to February 2017 in the United States. We compared the sentiment of patient experience tweets between Twitter users who self-identified as LGBTQ and non-LGBTQ. The effect of state-level partisan identity on patient experience sentiment and differences between LGBTQ users and non-LGBTQ users were analyzed. Results We observed lower (more negative) patient experience sentiment among 13,689 LGBTQ users compared to 1,362,395 non-LGBTQ users. Increasing state-level liberal political identification was associated with higher patient experience sentiment among all users but had stronger effects for LGBTQ users. Conclusions Our findings highlight that social media data can yield insights about patient experience for LGBTQ persons and suggest that a state-level sociopolitical environment influences patient experience for this group. Efforts are needed to reduce disparities in patient care for LGBTQ persons while taking into context the effect of the political climate on these inequities.

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