Relevance & Research Question:

Racism is a social practice encompassing both actions and rationales for action, which naturalize differences between humans and thus take for granted the objective reality of race (Fields & Fields 2012). In 2020, events such as the terrorist attack in Hanau and the death of George Floyd illustrated the omnipresence of racism in its different facets globally. Thus, a new debate about racism in society emerged, which was conducted on social media to a considerable extent. Both, the rise of hashtag-based activism and the emergence of filter bubbles attest to the importance of social media on societal discourses.

Methods & Data:

Our study is concerned with the systematic measurement of the prevalence and magnitude of ‘racist’ discourses. By analyzing social media text data from Twitter, we draw conclusions regarding how these discourses vary over time and region.

From October 2018 to this present day, we have used a database of nearly 1 billion German tweets (~1.1 million tweets per day). We employ a combination of word embedding models and topic modeling techniques to identify clusters that include discourse about racism (Sia 2020). We link information on regional time series data to augment our dataset with social structural information.

Results:

We find that the discourse about racism in Germany peaked in the Summer of 2020 and made up about 4% of all German tweets in that timeframe. Most notably, every third tweet regarding this discourse has been retweeted by other users, which is indicative of a highly active network structure. Analyses using the regional data reveal distinct spatial differences between regions of Germany, not only in its prevalence but also in the perception of the racist discourse. Regression models using social structural data can account for some of this regional variance.

Added Value:

Our approach allows us to detect changing trends and continuities of the racial and anti-racial discourse over 2.5 years separated by regional differences. Our rich data on an abundance of different topics enables us to connect the discourse about racism to closely related topics discussed on social media.

Researchers capitalizing on social media data to study public opinion aimed at creating point estimates like those elaborated by opinion surveys (e.g., Klašnja et al. 2018). By doing so, most attempts are directed toward whether social media data can predict election outcomes (see review from Rousidis et al. 2020). Other studies have investigated how the social media and public agendas from a representative public correlate and what affects the rhythms of attention (e.g., Stier et al. 2018). Our study is situated at the nexus of these two approaches and seeks to assess under what circumstances social media data can reliably complement survey data collection.

We rely on a two-year longitudinal data collection of tweets emitted by more than 100’000 identified Swiss users. We compare tweets with survey data across a range of topic areas.

In a first research step, we assess the extent to which Twitter data can validly reflect trends found in traditional public opinion measures, such as voting decisions in popular votes, and main political concerns. Concerning the former, text similarity measures between tweets about popular votes and open-ended survey pros and cons arguments about the same voting objects allow us to reflect the majority voting decision. Concerning the latter, we show a discrepancy between the off-line and on-line public agendas, especially in the ranking of the importance of policy concerns.

Beside the alignment of both data sources, there are numerous ways in which social media can complement survey data. Most notably, social media data are very reactive to events and can thus offer a useful complement to survey data for accounting for social movements. Namely, the timing of a survey might not always coincide with the timing of a protest. Our results reflect major “real-life” events (e.g., strikes and mobilizations) and allow us to extract salient aspects surrounding these events.

Our study disentangles circumstances in which social media reflect survey patterns, especially by looking at voting objects and main political concerns. It also insists on circumstances in which social media provide complementary insights to surveys, especially for social movement detection and analysis.

Relevance and research question:

Agenda-setting studies assume a correlation of agendas, in which media agenda influences the audiences' agendas. This assumption has been continuously challenged in the current multi-channel-online environment, where traditional media operate alongside social media accounts. Thus, scholars should posit that different audiences (perhaps even at the individual-user level) could form a “personal agenda-setting.” We explored the differences of the agenda topics salience among those exposed to content through different ‘follow-up’ patterns in online social networks.

Methods and data:

Respondents who represent the Israeli voters' population for the March 2020 elections were invited from an online panel to create a cluster sample. After a filtering question about using online social networks, this study is based on the answers of 448 respondents. The questionnaire examined the voting intentions, the topics on the respondents' agenda, and patterns of following candidates on online social networks: Facebook, Twitter, and Instagram.

Results:

When the prominent topics on the general agenda were examined, it was found that 48% of the respondents mentioned a security incident, 35% a health crisis, 22% a welfare issue, 20% an economic crisis, 17% a coalition formation. Nonetheless, considerable differences exist when inspecting the respondent's following patterns: for example, there is a significant difference (t = 1.74, p <.05) between those who follow politicians' accounts in social networks than those who do not. Among followers, the topic of ‘Welfare’ was ranked significantly higher. Respondents who follow politicians on Twitter tend to rank the ‘Economic crisis’ higher (t = 1.8, p <.05). There is a significant difference (t = 2.07, p <.05) between exclusive followers of the leading opposition candidate (Benjamin Gantz) concerning the topic of ‘Health’ which they ranked higher. Multivariant analyses were conducted to identify the personal agendas of specific topics concerning several combinations of the following patterns.

Added value:

This study implies that the traditional approach to agenda-setting research is less compatible for studying users’ agendas in online environments. A better understanding of online agendas' formation is paramount when examining users’ passive and active exposure to political content through social networks.