The present paper contributes to the research on the activities of farright actors on social media by examining the interconnections between far-right actors and groups on Telegram platform using network analysis. The far-right network observed on Telegram is highly decentralized, similarly to the far-right networks found on other social media platforms. The network is divided mostly along the ideological and national lines, with the communities related to 4chan imageboard and Donald Trump's supporters being the most influential. The analysis of the network evolution shows that the start of its explosive growth coincides in time with the mass bans of the far-right actors on mainstream social media platforms. The observed patterns of network evolution suggest that the simultaneous migration of these actors to Telegram has allowed them to swiftly recreate their connections and gain prominence in the network thus casting doubt on the effectiveness of deplatforming for curbing the influence of far-right and other extremist actors.

Access to accurate and up-to-date information is essential for individual and collective decision making, especially at times of emergency. On February 26, 2020, two weeks before the World Health Organization (WHO) officially declared the COVID-19's emergency a "pandemic," we systematically collected and analyzed search results for the term "coronavirus" in three languages from six search engines. We found that different search engines prioritize specific categories of information sources, such as governmentrelated websites or alternative media. We also observed that source ranking within the same search engine is subjected to randomization, which can result in unequal access to information among users.

No abstract

This article explores the issue of political polarization on social media. It shows that the intensity of polarization on Twitter varies greatly from one country to another. The analysis is performed using network-analytic audience duplication approach and is based on the data about the followers of the political parties’ Twitter accounts in 16 democratic countries. Based on the topology of the audience duplication graphs, the political Twitterspheres of the countries are classified as perfectly integrated, integrated, mixed, polarized and perfectly polarized. Explorative analysis shows that polarization is the highest in two-party systems with plurality electoral rules and the lowest in multi-party systems with proportional voting. The findings help explain the discrepancies in the results of previous studies into polarization on social media. The results of the study indicate that extrapolation of the findings from single-case studies on the topic is impossible in most cases, suggesting that more comparative studies on the matter are necessary to better understand the subject and get generalizable results.

PurposeThis study investigates perceptions of the use of online tracking, a passive data collection method relying on the automated recording of participant actions on desktop and mobile devices, for studying information behavior. It scrutinizes folk theories of tracking, the concerns tracking raises among the potential participants and design mechanisms that can be used to alleviate these concerns.Design/methodology/approachThis study uses focus groups composed of university students (n = 13) to conduct an in-depth investigation of tracking perceptions in the context of information behavior research. Each focus group addresses three thematic blocks: (1) views on online tracking as a research technique, (2) concerns that influence participants' willingness to be tracked and (3) design mechanisms via which tracking-related concerns can be alleviated. To facilitate the discussion, each focus group combines open questions with card-sorting tasks. The results are analyzed using a combination of deductive content analysis and constant comparison analysis, with the main coding categories corresponding to the thematic blocks listed above.FindingsThe study finds that perceptions of tracking are influenced by recent data-related scandals (e.g. Cambridge Analytica), which have amplified negative attitudes toward tracking, which is viewed as a surveillance tool used by corporations and governments. This study also confirms the contextual nature of tracking-related concerns, which vary depending on the activities and content that are tracked. In terms of mechanisms used to address these concerns, this study highlights the importance of transparency-based mechanisms, particularly explanations dealing with the aims and methods of data collection, followed by privacy- and control-based mechanisms.Originality/valueThe study conducts a detailed examination of tracking perceptions and discusses how this research method can be used to increase engagement and empower participants involved in information behavior research.

By filtering and ranking information, search engines shape how individuals perceive both the present and past events. However, these information curation mechanisms are prone to malperformance that can misinform their users. In this article, we examine how search malperformance can influence representation of traumatic past by investigating image search outputs of six search engines in relation to the Holocaust in English and Russian. Our findings indicate that besides two common themes - commemoration and liberation of camps - there is substantial variation in visual representation of the Holocaust between search engines and languages. We also observe several instances of search malperformance, including content propagating antisemitism and Holocaust denial, misattributed images, and disproportionate visibility of specific Holocaust aspects that might result in its distorted perception by the public.

We examine how six search engines filter and rank information in relation to the queries on the U.S. 2020 presidential primary elections under the default—that is nonpersonalized—conditions. For that, we utilize an algorithmic auditing methodology that uses virtual agents to conduct large-scale analysis of algorithmic information curation in a controlled environment. Specifically, we look at the text search results for “us elections,” “donald trump,” “joe biden,” “bernie sanders” queries on Google, Baidu, Bing, DuckDuckGo, Yahoo, and Yandex, during the 2020 primaries. Our findings indicate substantial differences in the search results between search engines and multiple discrepancies within the results generated for different agents using the same search engine. It highlights that whether users see certain information is decided by chance due to the inherent randomization of search results. We also find that some search engines prioritize different categories of information sources with respect to specific candidates. These observations demonstrate that algorithmic curation of political information can create information inequalities between the search engine users even under nonpersonalized conditions. Such inequalities are particularly troubling considering that search results are highly trusted by the public and can shift the opinions of undecided voters as demonstrated by previous research.

This study explores shifts in political trust during the outbreak of the COVID-19 pandemic in Switzerland, examining the role that media consumption and threat perceptions played in individuals’ trust in politics. We combine panel surveys taken before and during the first nation-wide lockdown with webtracking data of participants' online behaviour to paint a nuanced picture of media effects during the crisis. Our work has several findings. First, political trust, an attitude known for its stability, increased following lockdown. Second, consumption of mainstream news on COVID-19 directly hindered this increase, with those reading more news having lower over-time trust, while the relatively minor alternative news consumption had no direct effect on political trust. Third, threat perceptions a) to health and b) from the policy response to the pandemic, have strong and opposite effects on political trust, with threats to health increasing trust, and threats from the government policy response decreasing it. Lastly, these threat perceptions condition the effect of COVID-19 news consumption on political trust: perceptions of threat had the power to both exacerbate and mute the effect of media consumption on government trust during the pandemic. Notably, we show that the expected negative effect of alternative news on political trust only exists for those who did not think COVID-19 posed a threat to their health, while public service news consumption reduced the negative effect produced by government threat perceptions. The paper therefore advances our understanding of the nuanced nature of media effects, particularly as relates to alternative media, especially during moments of crisis.