Abstract
Social media platforms such as Twitter are considered a new mediator of collective action, in which various forms of civil movements unite around public posts, often using a common hashtag, thereby strengthening the movements. After 26 February 2018, the #AllforJan hashtag spread across the web when Ján Kuciak, a young journalist investigating corruption in Slovakia, and his fiancée were killed. The murder caused moral shock and mass protests in Slovakia and in several other European countries, as well. This paper investigates how this murder, and its follow-up events, were discussed on Twitter, in Europe, from 26 February to 15 March 2018. Our investigations, including spatiotemporal and sentiment analyses, combined with topic modeling, were conducted to comprehensively understand the trends and identify potential underlying factors in the escalation of the events. After a thorough data pre-processing including the extraction of spatial information from the users’ profile and the translation of non-English tweets, we clustered European countries based on the temporal patterns of tweeting activity in the analysis period and investigated how the sentiments of the tweets and the discussed topics varied over time in these clusters. Using this approach, we found that tweeting activity resonates not only with specific follow-up events, such as the funeral or the resignation of the Prime Minister, but in some cases, also with the political narrative of a given country affecting the course of discussions. Therefore, we argue that Twitter data serves as a unique and useful source of information for the analysis of such civil movements, as the analysis can reveal important patterns in terms of spatiotemporal and sentimental aspects, which may also help to understand protest escalation over space and time.
Highlights
The perception of inherent tensions between justice and injustice often press a group of people to seek change concerning politics and power, for example in the form of protests [1]
The starting date is adjusted to the first official report of the murder of Ján Kuciak while the final day is adapted to the earliest statement of the resignation of Prime Minister, Robert Fico
The semantic text analysis process used in our approach is divided into two stages: first, we extend the list of stop words in the algorithm based on the characteristics of our data set and we remove these words from the text, and second, we provide a dictionary-based sentiment analysis, which classifies the subjective sentiment information contained in each tweet
Summary
The perception of inherent tensions between justice and injustice (or the disproportion of good and bad) often press a group of people (or even the whole society) to seek change concerning politics and power, for example in the form of protests [1]. The data-driven approach, relying on social media posts and activities, has many strengths—especially considering its high temporal resolution and rapid user-response to certain news and information [3]. Social movement research employs this approach to identify the sentiment of the masses during an event to discover an individual’s inner tension [4,5]. Another component of the research is devoted to identifying the sociopolitical event (e.g., murder, accident, or price rise) that initiates a mass movement [6,7,8]. The message functions of various social media sites (e.g., message sharing or liking) can enable users to create an identifiable leader from (and for) the masses [14,15,16,17]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
