Search

The abundance of information sources in our digital environment makes it difficult to study how such information shapes individuals' attitudes towards current policies. Our study investigates how self-directed online search in a naturalistic setting through three randomized controlled experiments with 791 German participants on three topical policy issues: basic child support, renewable energy transition, and cannabis legalization. Participants' online browsing was passively tracked, and changes in their attitudes were measured. By encouraging participants to seek online information, this study enhances ecological validity compared to traditional experiments that expose subjects to predetermined content. Significant attitude shifts were observed for child support and cannabis legalization, but not for renewable energy transition. Some findings suggest that the specificity and granularity of policy topics may affect whether and how online information shapes political views, providing insights into the nuanced impact of online information seeking on policy attitudes. By exploring participant's searches and visits, we depict the behavioral patterns that emerge on from our encouragement. Our experimental approach lays the groundwork for future research to advance understanding of the media effect within the dynamic online information landscape.

As the exploration of digital behavioral data revolutionizes communication research, understanding the nuances of data collection methodologies becomes increasingly pertinent. This study focuses on one prominent data collection approach, web scraping, and more specifically, its application in the growing field of research relying on web browsing data. We investigate discrepancies between content obtained directly during user interaction with a website (in-situ) and content scraped using the URLs of participants' logged visits (ex-situ) with various time delays (0, 30, 60, and 90 days). We find substantial disparities between the methodologies, uncovering that errors are not uniformly distributed across news categories regardless of classification method (domain, URL, or content analysis). These biases compromise the precision of measurements used in existing literature. The ex-situ collection environment is the primary source of the discrepancies (~33.8%), while the time delays in the scraping process play a smaller role (adding ~6.5 percentage points in 90 days). Our research emphasizes the need for data collection methods that capture web content directly in the user's environment. However, acknowledging its complexities, we further explore strategies to mitigate biases in web-scraped browsing histories, offering recommendations for researchers who rely on this method and laying the groundwork for developing error-correction frameworks.

The conspiracy theory that the US 2020 presidential election was fraudulent - the Big Lie - remained a prominent part of the media agenda months after the election. Whether and how search engines prioritized news stories that sought to thoroughly debunk the claims, provide a simple negation, or support the conspiracy is crucial for understanding information exposure on the topic. We investigate how search engines provided news on this conspiracy by conducting a large-scale algorithm audit evaluating differences between three search engines (Google, DuckDuckGo, and Bing), across three locations (Ohio, California, and the UK), and using eleven search queries. Results show that simply denying the conspiracy is the largest debunking strategy across all search engines. While Google has a strong mainstreaming effect on articles explicitly focused on the Big Lie - providing thorough debunks and alternative explanations - DuckDuckGo and Bing display, depending on the location, a large share of articles either supporting the conspiracy or failing to debunk it. Lastly, we find that niche ideologically driven search queries (e.g., "sharpie marker ballots Arizona") do not lead to more conspiracy-supportive material. Instead, content supporting the conspiracy is largely a product of broader ideology-agnostic search queries (e.g., "voter fraud 2020")., Comment: 40 pages

Researchers in the political and social sciences often rely on classification models to analyze trends in information consumption by examining browsing histories of millions of webpages. Automated scalable methods are necessary due to the impracticality of manual labeling. In this paper, we model the detection of topic-related content as a binary classification task and compare the accuracy of fine-tuned pre-trained encoder models against in-context learning strategies. Using only a few hundred annotated data points per topic, we detect content related to three German policies in a database of scraped webpages. We compare multilingual and monolingual models, as well as zero and few-shot approaches, and investigate the impact of negative sampling strategies and the combination of URL & content-based features. Our results show that a small sample of annotated data is sufficient to train an effective classifier. Fine-tuning encoder-based models yields better results than in-context learning. Classifiers using both URL & content-based features perform best, while using URLs alone provides adequate results when content is unavailable.

This article evaluates the quality of data collection in individual-level desktop information tracking used in the social sciences and shows that the existing approaches face sampling issues, validity issues due to the lack of content-level data and their disregard of the variety of devices and long-tail consumption patterns as well as transparency and privacy issues. To overcome some of these problems, the article introduces a new academic tracking solution, WebTrack, an open source tracking tool maintained by a major European research institution. The design logic, the interfaces and the backend requirements for WebTrack, followed by a detailed examination of strengths and weaknesses of the tool, are discussed. Finally, using data from 1185 participants, the article empirically illustrates how an improvement in the data collection through WebTrack leads to new innovative shifts in the processing of tracking data. As WebTrack allows collecting the content people are exposed to on more than classical news platforms, we can strongly improve the detection of politics-related information consumption in tracking data with the application of automated content analysis compared to traditional approaches that rely on the list-based identification of news., Comment: 73 pages

Researchers rely on academic Web search engines to find scientific sources, but search engine mechanisms may selectively present content that aligns with biases embedded in queries. This study examines whether confirmation biased queries prompted into Google Scholar and Semantic Scholar will yield results aligned with a query's bias. Six queries (topics across health and technology domains such as vaccines, Internet use) were analyzed for disparities in search results. We confirm that biased queries (targeting benefits or risks) affect search results in line with bias, with technology-related queries displaying more significant disparities. Overall, Semantic Scholar exhibited fewer disparities than Google Scholar. Topics rated as more polarizing did not consistently show more disparate results. Academic search results that perpetuate confirmation bias have strong implications for both researchers and citizens searching for evidence. More research is needed to explore how scientific inquiry and academic search engines interact.

A major challenge of our time is reducing disparities in access to and effective use of digital technologies, with recent discussions highlighting the role of AI in exacerbating the digital divide. We examine user characteristics that predict usage of the AI-powered conversational agent ChatGPT. We combine behavioral and survey data in a web tracked sample of N=1376 German citizens to investigate differences in ChatGPT activity (usage, visits, and adoption) during the first 11 months from the launch of the service (November 30, 2022). Guided by a model of technology acceptance (UTAUT- 2), we examine the role of socio-demographics commonly associated with the digital divide in ChatGPT activity and explore further socio-political attributes identified via stability selection in Lasso regressions. We confirm that lower age and higher education affect ChatGPT usage, but do not find that gender or income do. We find full-time employment and more children to be barriers to ChatGPT activity. Using a variety of social media was positively associated with ChatGPT activity. In terms of political variables, political knowledge and political self-efficacy as well as some political behaviors such as voting, debating political issues online and offline and political action online were all associated with ChatGPT activity, with online political debating and political self-efficacy negatively so. Finally, need for cognition and communication skills such as writing, attending meetings, or giving presentations, were also associated with ChatGPT engagement, though chairing/organizing meetings was negatively associated. Our research informs efforts to address digital disparities and promote digital literacy among underserved populations by presenting implications, recommendations, and discussions on ethical and social issues of our findings.

The memorialization of mass atrocities such as war crimes and genocides facilitates the remembrance of past suffering, honors those who resisted the perpetrators, and helps prevent the distortion of historical facts. Digital technologies have transformed memorialization practices by enabling less top-down and more creative approaches to remember mass atrocities. At the same time, they may also facilitate the spread of denialism and distortion, attempt to justify past crimes and attack the dignity of victims. The emergence of generative forms of artificial intelligence (AI), which produce textual and visual content, has the potential to revolutionize the field of memorialization even further. AI can identify patterns in training data to create new narratives for representing and interpreting mass atrocities - and do so in a fraction of the time it takes for humans. The use of generative AI in this context raises numerous questions: For example, can the paucity of training data on mass atrocities distort how AI interprets some atrocity-related inquiries? How important is the ability to differentiate between human- and AI-made content concerning mass atrocities? Can AI-made content be used to promote false information concerning atrocities? This article addresses these and other questions by examining the opportunities and risks associated with using generative AIs for memorializing mass atrocities. It also discusses recommendations for AIs integration in memorialization practices to steer the use of these technologies toward a more ethical and sustainable direction., Comment: 22 pages

The 2020 US elections news coverage was extensive, with new pieces of information generated rapidly. This evolving scenario presented an opportunity to study the performance of search engines in a context in which they had to quickly process information as it was published. We analyze novelty, a measurement of new items that emerge in the top news search results, to compare the coverage and visibility of different topics. We conduct a longitudinal study of news results of five search engines collected in short-bursts (every 21 minutes) from two regions (Oregon, US and Frankfurt, Germany), starting on election day and lasting until one day after the announcement of Biden as the winner. We find more new items emerging for election related queries ("joe biden", "donald trump" and "us elections") compared to topical (e.g., "coronavirus") or stable (e.g., "holocaust") queries. We demonstrate differences across search engines and regions over time, and we highlight imbalances between candidate queries. When it comes to news search, search engines are responsible for such imbalances, either due to their algorithms or the set of news sources they rely on. We argue that such imbalances affect the visibility of political candidates in news searches during electoral periods.

In today's high-choice media environment, search engines play an integral role in informing individuals and societies about the latest events. The importance of search algorithms is even higher at the time of crisis, when users search for information to understand the causes and the consequences of the current situation and decide on their course of action. In our paper, we conduct a comparative audit of how different search engines prioritize visual information related to COVID-19 and what consequences it has for the representation of the pandemic. Using a virtual agent-based audit approach, we examine image search results for the term "coronavirus" in English, Russian and Chinese on five major search engines: Google, Yandex, Bing, Yahoo, and DuckDuckGo. Specifically, we focus on how image search results relate to generic news frames (e.g., the attribution of responsibility, human interest, and economics) used in relation to COVID-19 and how their visual composition varies between the search engines., Comment: 18 pages, 1 figure, 3 tables

Online platforms have transformed the way in which individuals access and interact with news, with a high degree of trust particularly placed in search engine results. We use web tracked behavioral data across a 2-month period and analyze three competing factors, two algorithmic (ranking and representativeness) and one psychological (familiarity) that could influence the selection of news articles that appear in search results. Participants' (n=280) news engagement is our proxy for familiarity, and we investigate news articles presented on Google search pages (n=1221). Our results demonstrate the steering power of the algorithmic factors on news consumption as compared to familiarity. But despite the strong effect of ranking, we find that it plays a lesser role for news articles compared to non-news. We confirm that Google Search drives individuals to unfamiliar sources and find that it increases the diversity of the political audience to news sources. With our methodology, we take a step in tackling the challenges of testing social science theories in digital contexts shaped by algorithms.

Web search engines are important online information intermediaries that are frequently used and highly trusted by the public despite multiple evidence of their outputs being subjected to inaccuracies and biases. One form of such inaccuracy, which so far received little scholarly attention, is the presence of conspiratorial information, namely pages promoting conspiracy theories. We address this gap by conducting a comparative algorithm audit to examine the distribution of conspiratorial information in search results across five search engines: Google, Bing, DuckDuckGo, Yahoo and Yandex. Using a virtual agent-based infrastructure, we systematically collect search outputs for six conspiracy theory-related queries (flat earth, new world order, qanon, 9/11, illuminati, george soros) across three locations (two in the US and one in the UK) and two observation periods (March and May 2021). We find that all search engines except Google consistently displayed conspiracy-promoting results and returned links to conspiracy-dedicated websites in their top results, although the share of such content varied across queries. Most conspiracy-promoting results came from social media and conspiracy-dedicated websites while conspiracy-debunking information was shared by scientific websites and, to a lesser extent, legacy media. The fact that these observations are consistent across different locations and time periods highlight the possibility of some search engines systematically prioritizing conspiracy-promoting content and, thus, amplifying their distribution in the online environments.

Web search engines influence perception of social reality by filtering and ranking information. However, their outputs are often subjected to bias that can lead to skewed representation of subjects such as professional occupations or gender. In our paper, we use a mixed-method approach to investigate presence of race and gender bias in representation of artificial intelligence (AI) in image search results coming from six different search engines. Our findings show that search engines prioritize anthropomorphic images of AI that portray it as white, whereas non-white images of AI are present only in non-Western search engines. By contrast, gender representation of AI is more diverse and less skewed towards a specific gender that can be attributed to higher awareness about gender bias in search outputs. Our observations indicate both the the need and the possibility for addressing bias in representation of societally relevant subjects, such as technological innovation, and emphasize the importance of designing new approaches for detecting bias in information retrieval systems., Comment: 16 pages, 3 figures

Algorithm audits have increased in recent years due to a growing need to independently assess the performance of automatically curated services that process, filter, and rank the large and dynamic amount of information available on the internet. Among several methodologies to perform such audits, virtual agents stand out because they offer the ability to perform systematic experiments, simulating human behaviour without the associated costs of recruiting participants. Motivated by the importance of research transparency and replicability of results, this paper focuses on the challenges of such an approach. It provides methodological details, recommendations, lessons learned, and limitations based on our experience of setting up experiments for eight search engines (including main, news, image and video sections) with hundreds of virtual agents placed in different regions. We demonstrate the successful performance of our research infrastructure across multiple data collections, with diverse experimental designs, and point to different changes and strategies that improve the quality of the method. We conclude that virtual agents are a promising venue for monitoring the performance of algorithms across long periods of time, and we hope that this paper can serve as a basis for further research in this area.

We audit the presence of domain-level source diversity bias in video search results. Using a virtual agent-based approach, we compare outputs of four Western and one non-Western search engines for English and Russian queries. Our findings highlight that source diversity varies substantially depending on the language with English queries returning more diverse outputs. We also find disproportionately high presence of a single platform, YouTube, in top search outputs for all Western search engines except Google. At the same time, we observe that Youtube's major competitors such as Vimeo or Dailymotion do not appear in the sampled Google's video search results. This finding suggests that Google might be downgrading the results from the main competitors of Google-owned Youtube and highlights the necessity for further studies focusing on the presence of own-content bias in Google's search results.

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" and "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.

With this work, we present a publicly available dataset of the history of all the references (more than 55 million) ever used in the English Wikipedia until June 2019. We have applied a new method for identifying and monitoring references in Wikipedia, so that for each reference we can provide data about associated actions: creation, modifications, deletions, and reinsertions. The high accuracy of this method and the resulting dataset was confirmed via a comprehensive crowdworker labelling campaign. We use the dataset to study the temporal evolution of Wikipedia references as well as users' editing behaviour. We find evidence of a mostly productive and continuous effort to improve the quality of references: (1) there is a persistent increase of reference and document identifiers (DOI, PubMedID, PMC, ISBN, ISSN, ArXiv ID), and (2) most of the reference curation work is done by registered humans (not bots or anonymous editors). We conclude that the evolution of Wikipedia references, including the dynamics of the community processes that tend to them should be leveraged in the design of relevance indexes for altmetrics, and our dataset can be pivotal for such effort.