arXiv subjects

Juhi Kulshrestha

Publications and source records attributed to Juhi Kulshrestha.

18 recordsLinked to original sources

arXiv2026

Depressive symptoms are reflected differently across digital contexts

As more of everyday life takes place online, digital behavior may provide a potential window into how depressive symptoms are reflected in daily life. Yet digital mental health studies have produced mixed findings. These inconsistencies may partly reflect how digital behavior is measured: self reported use, single device studies, and aggregate screen time can obscure differences across devices, activities, and patterns of engagement. We combined monthly assessments of depressive symptoms with passively recorded mobile and desktop web traces from 1,146 adults in Germany over six months. We examined how general, cognitive-affective, and somatic depressive symptoms are reflected across digital contexts defined by device and activity type. Associations varied markedly across these contexts. On mobile, more severe symptoms were associated with more nighttime activity, greater use of social media, messaging, and entertainment, and fewer but longer sessions. On desktop, associations were fewer and largely involved reduced engagement with news, shopping, and adult content. Mobile associations primarily arose for general and cognitive-affective symptoms, whereas desktop associations were concentrated in somatic symptoms. Our findings suggest that characterizing how depressive symptoms are reflected in digital behavior requires attending to what people do online and where, not only how much screens are used.

cs.HC↗

arXiv2026

Competing for a Finite Pool of Attention in Social Media? How a New Geopolitical Conflict Reshapes Engagement in Bluesky

Major geopolitical crises can rapidly reshape online public attention. Yet population-level increases in discussion volume about a new crisis reveal little about how users accommodate this new demand for attention. We study the onset of the Iran-US-Israel conflict, triggered on 28 February 2026, using longitudinal repost activity from Bluesky across four consecutive approximately three-month windows spanning the period before and after its onset; the data comprise 91.0 million unique posts and 645.5 million repost observations. We find that the new conflict reorganized participation through both reallocation among existing conflict participants and substantial activation of previously low-conflict-active users, while some previously active users reduced their conflict-related participation. Attention redistribution differed substantially across pre-existing interests: Iran-US-Israel and Israel-Palestine attention showed strong positive co-movement with little systematic relative replacement, whereas Other Political and Non-Political content more consistently lost attention share, and Russia-Ukraine exhibited weaker, heterogeneous replacement. Finally, disruption of users' broader attention allocation was substantially more prevalent among users with established attention to geopolitical conflicts than in the overall or Non-Political populations. Together, these findings show that a newly emerging conflict reorganizes online attention through turnover in who participates, selective co-attendance or replacement across topics, and disruption of broader attention patterns concentrated among users already engaged with geopolitical conflicts.

cs.SI↗

arXiv2026

Self-directed online information search can affect policy support: a randomized encouragement design with digital behavioral data

As citizens increasingly encounter political information in digital environments, understanding whether this engagement shapes their policy views has become a central concern. Drawing on dual-process theories of persuasion, we argue that motivational activation is an enabling condition for policy support change in high-choice online environments. We test this in a three-wave field experiment with German participants (n = 791) across three policy topics (basic child support, renewable energy transition, cannabis legalization), in which participants were randomly assigned to a control group, and two encouragement conditions: a verbal encouragement, or a monetary incentive tied to a knowledge test. Browsing behavior was passively tracked via digital trace data over a 20-hour window. We find that self-directed online information search produced changes in policy support for child support and cannabis legalization but not for the energy transition, with monetary incentives producing significant effects rather than verbal prompts. We discuss motivational salience, issue malleability, and search-environment quality as joint conditions under which political information engagement can produce detectable changes in policy support.

cs.CY↗

arXiv2025

Lonely Individuals Show Distinct Patterns of Social Media Engagement

Loneliness has reached epidemic proportions globally, posing serious risks to mental and physical health. As social media platforms increasingly mediate social interaction, understanding their relationship with loneliness has become urgent. While survey-based research has examined social media use and loneliness, findings remain mixed, and little is known about when and how often people engage with social media, or about whether different types of platforms are differently associated with loneliness. Web trace data now enable objective examination of these behavioral dimensions. We asked whether objectively measured patterns of social media engagement differ between lonely and non-lonely individuals across devices and platform types. Analyzing six months of web trace data combined with repeated surveys ($N=589$ mobile users; $N=851$ desktop users), we found that greater social media use was associated with higher loneliness across both devices, with this relationship specific to social media rather than other online activities. On desktop, lonely individuals exhibited shorter sessions but more frequent daily engagement. Lonely individuals spent more time on visual-sharing ($g = -0.47$), messaging ($g = -0.36$), and networking-oriented platforms on mobile. These findings demonstrate how longitudinal web trace data can reveal behavioral patterns associated with loneliness, and more broadly illustrate the potential of digital traces for studying other psychological states. Beyond research, the results inform the responsible design of digital interventions and platform features that better support psychological well-being across different technological contexts.

cs.HC↗

arXiv2025

Characteristics of ChatGPT users from Germany: implications for the digital divide from web tracking data

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.

cs.CY↗

arXiv2025

Opting Out of Generative AI: a Behavioral Experiment on the Role of Education in Perplexity AI Avoidance

The rise of conversational AI (CAI), powered by large language models, is transforming how individuals access and interact with digital information. However, these tools may inadvertently amplify existing digital inequalities. This study investigates whether differences in formal education are associated with CAI avoidance, leveraging behavioral data from an online experiment (N = 1,636). Participants were randomly assigned to a control or an information-seeking task, either a traditional online search or a CAI (Perplexity AI). Task avoidance (operationalized as survey abandonment or providing unrelated responses during task assignment) was significantly higher in the CAI group (51%) compared to the search (30.9%) and control (16.8%) groups, with the highest CAI avoidance among participants with lower education levels (~74.4%). Structural equation modeling based on the theoretical framework UTAUT2 and LASSO regressions reveal that education is strongly associated with CAI avoidance, even after accounting for various cognitive and affective predictors of technology adoption. These findings underscore education's central role in shaping AI adoption and the role of self-selection biases in AI-related research, stressing the need for inclusive design to ensure equitable access to emerging technologies.

cs.CY↗

arXiv2025

Stress Bytes: Decoding the Associations between Internet Use and Perceived Stress

In today's digital era, internet plays a pervasive role in our lives, influencing everyday activities such as communication, work, and leisure. This online engagement intertwines with offline experiences, shaping individuals' overall well-being. Despite its significance, existing research often falls short in capturing the relationship between internet use and well-being, relying primarily on isolated studies and self-reported data. One of the major contributors to deteriorated well-being - both physical and mental - is stress. While some research has examined the relationship between internet use and stress, both positive and negative associations have been reported. Our primary goal in this work is to identify the associations between an individual's internet use and their stress. For achieving our goal, we conducted a longitudinal multimodal study that spanned seven months. We combined fine-grained URL-level web browsing traces of 1490 German internet users with their sociodemographics and monthly measures of stress. Further, we developed a conceptual framework that allows us to simultaneously explore different contextual dimensions, including how, where, when, and by whom the internet is used. Our analysis revealed several associations between internet use and stress that vary by context. Social media, entertainment, online shopping, and gaming were positively associated with stress, while productivity, news, and adult content use were negatively associated. In the future, the behavioral markers we identified can pave the way for designing individualized tools for people to self-monitor and self-moderate their online behaviors to enhance their well-being, reducing the burden on already overburdened mental health services.

cs.HC↗

arXiv2024

Browsing behavior exposes identities on the Web

How easy is it to uniquely identify a person based solely on their web browsing behavior? Here we show that when people navigate the Web, their online traces produce fingerprints that identify them. Merely the four most visited web domains are enough to identify 95% of the individuals. These digital fingerprints are stable and render high re-identifiability. We demonstrate that we can re-identify 80% of the individuals in separate time slices of data. Such a privacy threat persists even with limited information about individuals' browsing behavior, reinforcing existing concerns around online privacy.

cs.CY↗

arXiv2023

Gender, Age, and Technology Education Influence the Adoption and Appropriation of LLMs

Large Language Models (LLMs) such as ChatGPT have become increasingly integrated into critical activities of daily life, raising concerns about equitable access and utilization across diverse demographics. This study investigates the usage of LLMs among 1,500 representative US citizens. Remarkably, 42% of participants reported utilizing an LLM. Our findings reveal a gender gap in LLM technology adoption (more male users than female users) with complex interaction patterns regarding age. Technology-related education eliminates the gender gap in our sample. Moreover, expert users are more likely than novices to list professional tasks as typical application scenarios, suggesting discrepancies in effective usage at the workplace. These results underscore the importance of providing education in artificial intelligence in our technology-driven society to promote equitable access to and benefits from LLMs. We urge for both international replication beyond the US and longitudinal observation of adoption.

cs.CY↗

arXiv2023

Sleep during COVID-19 pandemic: Longitudinal observational study combining multisensor data with questionnaires

The COVID-19 pandemic led to various containment strategies, such as work-from-home policies and reduced social contact, which significantly altered people's sleep patterns. Our study, conducted from June 2021 to June 2022, longitudinally examined the changes in the sleep patterns of working adults in Finland during this period, utilizing multisensor data from fitness trackers and monthly questionnaires. We conducted a comprehensive study, exploring the changes in sleep patterns in correlation with multiple factors such as individual demographics, occupation, sleep-related behaviors, levels of physical activity, restrictions imposed by the pandemic, and adjustments in seasonal variations. From over 27,000 nights analyzed from 112 participants, we found a correlation between stringent pandemic measures and increased total sleep time as well as delayed sleep timing. Academic staff experienced shorter and more variable sleep durations compared to service staff. Early-day physical activity was also linked to longer sleep duration, revealing the influence of lifestyle on sleep quality. Habitual snoozers exhibited higher variability in their sleep patterns. The findings reveal the multifaceted impacts of the pandemic and associated measures on sleep patterns, highlighting the nuanced variations among different occupations and habits, and emphasizing the role of flexible work-life routines and external factors in shaping sleep behaviors during such unprecedented times.

cs.HC↗

arXiv2022

Novelty in news search: a longitudinal study of the 2020 US elections

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.

cs.CY↗

arXiv2022

A Domain-adaptive Pre-training Approach for Language Bias Detection in News

Media bias is a multi-faceted construct influencing individual behavior and collective decision-making. Slanted news reporting is the result of one-sided and polarized writing which can occur in various forms. In this work, we focus on an important form of media bias, i.e. bias by word choice. Detecting biased word choices is a challenging task due to its linguistic complexity and the lack of representative gold-standard corpora. We present DA-RoBERTa, a new state-of-the-art transformer-based model adapted to the media bias domain which identifies sentence-level bias with an F1 score of 0.814. In addition, we also train, DA-BERT and DA-BART, two more transformer models adapted to the bias domain. Our proposed domain-adapted models outperform prior bias detection approaches on the same data.

cs.CL↗

arXiv2021

Where the Earth is flat and 9/11 is an inside job: A comparative algorithm audit of conspiratorial information in web search results

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.

cs.IR↗

arXiv2021

Misinformation, Believability, and Vaccine Acceptance Over 40 Countries: Takeaways From the Initial Phase of The COVID-19 Infodemic

The COVID-19 pandemic has been damaging to the lives of people all around the world. Accompanied by the pandemic is an infodemic, an abundant and uncontrolled spreading of potentially harmful misinformation. The infodemic may severely change the pandemic's course by interfering with public health interventions such as wearing masks, social distancing, and vaccination. In particular, the impact of the infodemic on vaccination is critical because it holds the key to reverting to pre-pandemic normalcy. This paper presents findings from a global survey on the extent of worldwide exposure to the COVID-19 infodemic, assesses different populations' susceptibility to false claims, and analyzes its association with vaccine acceptance. Based on responses gathered from over 18,400 individuals from 40 countries, we find a strong association between perceived believability of misinformation and vaccination hesitancy. Additionally, our study shows that only half of the online users exposed to rumors might have seen the fact-checked information. Moreover, depending on the country, between 6% and 37% of individuals considered these rumors believable. Our survey also shows that poorer regions are more susceptible to encountering and believing COVID-19 misinformation. We discuss implications of our findings on public campaigns that proactively spread accurate information to countries that are more susceptible to the infodemic. We also highlight fact-checking platforms' role in better identifying and prioritizing claims that are perceived to be believable and have wide exposure. Our findings give insights into better handling of risk communication during the initial phase of a future pandemic.

cs.SI↗

arXiv2020

Web Routineness and Limits of Predictability: Investigating Demographic and Behavioral Differences Using Web Tracking Data

Understanding human activities and movements on the Web is not only important for computational social scientists but can also offer valuable guidance for the design of online systems for recommendations, caching, advertising, and personalization. In this work, we demonstrate that people tend to follow routines on the Web, and these repetitive patterns of web visits increase their browsing behavior's achievable predictability. We present an information-theoretic framework for measuring the uncertainty and theoretical limits of predictability of human mobility on the Web. We systematically assess the impact of different design decisions on the measurement. We apply the framework to a web tracking dataset of German internet users. Our empirical results highlight that individual's routines on the Web make their browsing behavior predictable to 85% on average, though the value varies across individuals. We observe that these differences in the users' predictabilities can be explained to some extent by their demographic and behavioral attributes.

cs.CY↗

arXiv2017

Characterizing Information Diets of Social Media Users

With the widespread adoption of social media sites like Twitter and Facebook, there has been a shift in the way information is produced and consumed. Earlier, the only producers of information were traditional news organizations, which broadcast the same carefully-edited information to all consumers over mass media channels. Whereas, now, in online social media, any user can be a producer of information, and every user selects which other users she connects to, thereby choosing the information she consumes. Moreover, the personalized recommendations that most social media sites provide also contribute towards the information consumed by individual users. In this work, we define a concept of information diet -- which is the topical distribution of a given set of information items (e.g., tweets) -- to characterize the information produced and consumed by various types of users in the popular Twitter social media. At a high level, we find that (i) popular users mostly produce very specialized diets focusing on only a few topics; in fact, news organizations (e.g., NYTimes) produce much more focused diets on social media as compared to their mass media diets, (ii) most users' consumption diets are primarily focused towards one or two topics of their interest, and (iii) the personalized recommendations provided by Twitter help to mitigate some of the topical imbalances in the users' consumption diets, by adding information on diverse topics apart from the users' primary topics of interest.

cs.SI↗

arXiv2017

Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media

Search systems in online social media sites are frequently used to find information about ongoing events and people. For topics with multiple competing perspectives, such as political events or political candidates, bias in the top ranked results significantly shapes public opinion. However, bias does not emerge from an algorithm alone. It is important to distinguish between the bias that arises from the data that serves as the input to the ranking system and the bias that arises from the ranking system itself. In this paper, we propose a framework to quantify these distinct biases and apply this framework to politics-related queries on Twitter. We found that both the input data and the ranking system contribute significantly to produce varying amounts of bias in the search results and in different ways. We discuss the consequences of these biases and possible mechanisms to signal this bias in social media search systems' interfaces.

cs.SI↗

arXiv2016

The Road to Popularity: The Dilution of Growing Audience on Twitter

On social media platforms, like Twitter, users are often interested in gaining more influence and popularity by growing their set of followers, aka their audience. Several studies have described the properties of users on Twitter based on static snapshots of their follower network. Other studies have analyzed the general process of link formation. Here, rather than investigating the dynamics of this process itself, we study how the characteristics of the audience and follower links change as the audience of a user grows in size on the road to user's popularity. To begin with, we find that the early followers tend to be more elite users than the late followers, i.e., they are more likely to have verified and expert accounts. Moreover, the early followers are significantly more similar to the person that they follow than the late followers. Namely, they are more likely to share time zone, language, and topics of interests with the followed user. To some extent, these phenomena are related with the growth of Twitter itself, wherein the early followers tend to be the early adopters of Twitter, while the late followers are late adopters. We isolate, however, the effect of the growth of audiences consisting of followers from the growth of Twitter's user base itself. Finally, we measure the engagement of such audiences with the content of the followed user, by measuring the probability that an early or late follower becomes a retweeter.

cs.SI↗