Users’ polarisation in dynamic discussion networks: The case of refugee crisis in Sweden

Abstract

This paper presents a study on the dynamics of sentiment polarisation in the active online discussion communities formed around a controversial topic—immigration. Using a collection of tweets in the Swedish language from 2012 to 2019, we track the development of the communities and their sentiment polarisation trajectories over time and in the context of an exogenous shock represented by the European refugee crisis in 2015. To achieve the goal of the study, we apply methods of network and sentiment analysis to map users’ interactions in the network communities and quantify users’ sentiment polarities. The results of the analysis give little evidence for users’ polarisation in the network and its communities, as well as suggest that the crisis had a limited effect on the polarisation dynamics on this social media platform. Yet, we notice a shift towards more negative tonality of users’ sentiments after the crisis and discuss possible explanations for the above-mentioned observations.

Trade unions, refugees and immigrant labour: Has the attitude changed? The stance of Swedish blue‐collar trade unions as evidenced by sentiment analysis

“…What deserves to be noted, though, is that similarly to previous studies (Rosetti, 2019) we see that the dispersion of sentiment values has become smaller in the wake of the crisis. In relation to the theoretical assumptions, we suggest that the migration crises could cause short‐term polarisation of attitude towards in‐migration, which is in line with previously found evidence (Kopacheva & Yantseva, 2022). As we focus on blue‐collar trade unions, it might be that the findings by Ryan and Turner (2021) on the effects of average educational attainment are relevant here.…”

Immigrant-critical alternative media in online conversations

“…However, our previously performed experiments demonstrated its reasonable performance for social media texts in Swedish, with an accuracy of 62%, which is on par with the previously performed tests (e.g. see [ 72 ]).…”

Digital news ecology and polarization in the context of deep learning: empowerment, entrenchment and reconciling failure

“…The reason is that the information products shared by the public are often structured and standardized, setting the framework and boundaries for social discussions so that extreme views are exhausted due to the lack of sufficient discursive resources [9][10]. However, in the digital news ecosystem, the standardized form of news has been disrupted, coupled with the filter bubble effect brought about by the personalization of news distribution [11][12]. The biases initially held by users are often further encouraged to move in more extreme directions, which ultimately leads to the formation of extreme viewpoint distributions and group expression structures and induces large-scale discursive violence under specific social conditions [13][14].…”