The clash of empires: Regulating technological threats to civil society

This paper examines the regulation of technology platform companies providing a platform for user-generated media content while playing an increasingly dominant role in the global flow of news and information. In doing so, platform companies play a crucial role in modern civic life, by deciding which content will reach users, engage the public\u27s attention, and be deemed credible. It is therefore crucial that we choose means of regulation that foster democratic values and robust civic engagement. In this paper we focus on the regulation of ‘computational propaganda\u27, including misinformation and ‘fake news\u27, the rise of synthetic media and so-called ‘deep fakes\u27, and novel forms of algorithmic injustice, such as the manipulation of search engine results and their effect on elections. We argue that many existing regulations fall short in that they adopt an approach that views regulation as a battle between two competing powers, or ‘empires’–that of the regulatory state versus the big tech companies. Accordingly, they approach regulation as a means of redistributing power between these two players, while discounting the end user, and they often involve unjustified restrictions of free speech through the imposition of content controls. © 2020 Informa UK Limited, trading as Taylor & Francis Group

Viewpoint Diversity in Search Results

Adverse phenomena such as the search engine manipulation effect (SEME), where web search users change their attitude on a topic following whatever most highly-ranked search results promote, represent crucial challenges for research and industry. However, the current lack of automatic methods to comprehensively measure or increase viewpoint diversity in search results complicates the understanding and mitigation of such effects. This paper proposes a viewpoint bias metric that evaluates the divergence from a pre-defined scenario of ideal viewpoint diversity considering two essential viewpoint dimensions (i.e., stance and logic of evaluation). In a case study, we apply this metric to actual search results and find considerable viewpoint bias in search results across queries, topics, and search engines that could lead to adverse effects such as SEME. We subsequently demonstrate that viewpoint diversity in search results can be dramatically increased using existing diversification algorithms. The methods proposed in this paper can assist researchers and practitioners in evaluating and improving viewpoint diversity in search results.</p

Search Bias Quantification: Investigating Political Bias in Social Media and Web Search

Users frequently use search systems on the Web as well as online social media to learn about ongoing events and public opinion on personalities. Prior studies have shown that the top-ranked results returned by these search engines can shape user opinion about the topic (e.g., event or person) being searched. In case of polarizing topics like politics, where multiple competing perspectives exist, the political bias in the top search results can play a significant role in shaping public opinion towards (or away from) certain perspectives. Given the considerable impact that search bias can have on the user, we propose a generalizable search bias quantification framework that not only measures the political bias in ranked list output by the search system but also decouples the bias introduced by the different sources—input data and ranking system. We apply our framework to study the political bias in searches related to 2016 US Presidential primaries in Twitter social media search and find that both input data and ranking system matter in determining the final search output bias seen by the users. And finally, we use the framework to compare the relative bias for two popular search systems—Twitter social media search and Google web search—for queries related to politicians and political events. We end by discussing some potential solutions to signal the bias in the search results to make the users more aware of them.publishe

Evaluation metrics for measuring bias in search engine results

Search engines decide what we see for a given search query. Since many people are exposed to information through search engines, it is fair to expect that search engines are neutral. However, search engine results do not necessarily cover all the viewpoints of a search query topic, and they can be biased towards a specific view since search engine results are returned based on relevance, which is calculated using many features and sophisticated algorithms where search neutrality is not necessarily the focal point. Therefore, it is important to evaluate the search engine results with respect to bias. In this work we propose novel web search bias evaluation measures which take into account the rank and relevance. We also propose a framework to evaluate web search bias using the proposed measures and test our framework on two popular search engines based on 57 controversial query topics such as abortion, medical marijuana, and gay marriage. We measure the stance bias (in support or against), as well as the ideological bias (conservative or liberal). We observe that the stance does not necessarily correlate with the ideological leaning, e.g. a positive stance on abortion indicates a liberal leaning but a positive stance on Cuba embargo indicates a conservative leaning. Our experiments show that neither of the search engines suffers from stance bias. However, both search engines suffer from ideological bias, both favouring one ideological leaning to the other, which is more significant from the perspective of polarisation in our society

A study of the web visibility of the SDGs and the 2030 Agenda on university websites

Universities play an important role in the promotion and implementation of the 2030 Agenda for Sustainable Development. This study examines the visibility of information about the Sustainable Development Goals (SDGs) on the websites of Spanish and major international universities, by means of a quantitative and qualitative analysis with an online visibility management platform that makes use of big data technology

Search engine effects on news consumption: Ranking and representativeness outweigh familiarity in news selection

While individuals' trust in search engine results is well-supported, little is known about their preferences when selecting news. We use web-tracked behavioral data across a 2-month period (280 participants) and we analyze three competing factors, two algorithmic (ranking and representativeness) and one psychological (familiarity), that could influence the selection of search results. We use news engagement as a proxy for familiarity and investigate news articles presented on Google search pages (n = 1221). We find a significant effect of algorithmic factors but not of familiarity. We find that ranking plays a lesser role for news compared to non-news, suggesting a more careful decision-making process. We confirm that Google Search drives individuals to unfamiliar sources, and find that it increases the diversity of the political audience of news sources. We tackle the challenge of measuring social science theories in contexts shaped by algorithms, demonstrating their leverage over the behaviors of individuals

Representativeness and face-ism: Gender bias in image search

Implicit and explicit gender biases in media representations of individuals have long existed. Women are less likely to be represented in gender-neutral media content (representation bias), and their face-to-body ratio in images is often lower (face-ism bias). In this article, we look at representativeness and face-ism in search engine image results. We systematically queried four search engines (Google, Bing, Baidu, Yandex) from three locations, using two browsers and in two waves, with gender-neutral (person, intelligent person) and gendered (woman, intelligent woman, man, intelligent man) terminology, accessing the top 100 image results. We employed automatic identification for the individual’s gender expression (female/male) and the calculation of the face-to-body ratio of individuals depicted. We find that, as in other forms of media, search engine images perpetuate biases to the detriment of women, confirming the existence of the representation and face-ism biases. In-depth algorithmic debiasing with a specific focus on gender bias is overdue

Trustworthiness Evaluations of Search Results: The Impact of Rank and Misinformation

Users rely on search engines for information in critical contexts, such as public health emergencies. Understanding how users evaluate the trustworthiness of search results is therefore essential. Research has identified rank and the presence of misinformation as factors impacting perceptions and click behavior in search. Here, we elaborate on these findings by measuring the effects of rank and misinformation, as well as warning banners, on the perceived trustworthiness of individual results in search. We conducted three online experiments (N=3196) using Covid-19-related queries to address this question. We show that although higher-ranked results are clicked more often, they are not more trusted. We also show that misinformation did not change trust in accurate results below it. However, a warning about unreliable sources backfired, decreasing trust in accurate information but not misinformation. This work addresses concerns about how people evaluate information in search, and illustrates the dangers of generic prevention approaches.Comment: 24 pages, 10 figures, 4 supplementary file

What Drives the Selection of Political Information on Google? Tension Between Ideal Democracy and the Influence of Ranking

The emergence of the Internet has altered how individuals obtain information—this also applies to political information. Search engines have taken over the role of political infor- mation gatekeepers, thus becoming key players in democ- racy. However, surprisingly little is known about the role of search engines in the political information process, that is, whether they represent an opportunity or a threat to democ- racy. Through an online survey experiment, which mimicked a Google web interface, this study examines how Swiss citizens select political information on a political news event from a Google search results page. Although citizens consider textual cues from snippets, they are more likely to select sources of information from the top of a Google results page, regardless of the source. We discuss these findings from a democratic theory perspective