arXiv subjects

Anastasia Sergeeva

Publications and source records attributed to Anastasia Sergeeva.

10 recordsLinked to original sources

arXiv2026

Comparing and Conceptualizing Data Protection Requirements Worldwide for Privacy Regulatory Compliance

The growing digitalization of society has intensified the collection, processing, and sharing of personal data, increasingly moving across national borders and regulatory jurisdictions, prompting a proliferation of data protection frameworks worldwide. These transborder personal data flows (TPDF) are essential to today's economy, but organizations managing them must reconcile data protection requirements that differ, sometimes subtly, across jurisdictions. For requirements engineering, this is the central challenge: regulatory data protection requirements (RDPRs) are complex and not directly translatable into software requirements, especially when frameworks impose similar, non-identical, or contradictory obligations. Identifying which requirements are shared and which diverge is therefore critical to managing TPDF, and addressing them late in the software development lifecycle (SDLC) causes costly rework, making early identification essential for compliance and stakeholder communication. This paper identifies and conceptualizes common RDPRs worldwide from the perspective of data protection legal experts, answering: (SQ1) which requirements are common across regulations and how are they conceptualized, and (SQ2) which requirements diverge and how do they differ conceptually. We combine deductive qualitative analysis of interviews with 70 legal experts from G20 economies and other countries and systematic content analysis of these economies' data protection regulations. We identify common requirements, such as consent, and divergent ones, such as the right to be forgotten. Given their impact across the SDLC and enterprise architecture, we translate these findings into a set of Data Protection Officer DPO (DPO) stories, using the user story notation, classified by SDLC phase and enterprise architecture layer, to help organizations manage TPDF compliance.

cs.SE↗

arXiv2025

Breaking the News: Taking the Roles of Influencer vs. Journalist in a LLM-Based Game for Raising Misinformation Awareness

Effectively mitigating online misinformation requires understanding of their mechanisms and learning of practical skills for identification and counteraction. Serious games may serve as tools for combating misinformation, teaching players to recognize common misinformation tactics, and improving their skills of discernment. However, current interventions are designed as single-player, choice-based games, which present players with limited predefined choices. Such restrictions reduce replayability and may lead to an overly simplistic understanding of misinformation and how to debunk them. This study seeks to empower people to understand opinion-influencing and misinformation-debunking processes. We created a Player vs. Player (PvP) game in which participants attempt to generate or debunk misinformation to convince the public opinion represented by LLM. Using a within-subjects mixed-methods study design (N=47), we found that this game significantly raised participants' media literacy and improved their ability to identify misinformation. Qualitative analyses revealed how participants' use of debunking and content creation strategies deepened their understanding of misinformation. This work shows the potential for illuminating contrasting viewpoints of social issues by LLM-based mechanics in PvP games.

cs.HC↗

arXiv2025

Shots and Boosters: Exploring the Use of Combined Prebunking Interventions to Raise Critical Thinking and Create Long-Term Protection Against Misinformation

The problem of how to effectively mitigate the flow of misinformation remains a significant challenge. The classical approach to this is public disapproval of claims or "debunking." The approach is still widely used on social media, but it has some severe limitations in terms of applicability and efficiency. An alternative strategy is to enhance individuals' critical thinking through educational interventions. Instead of merely disproving misinformation, these approaches aim to strengthen users' reasoning skills, enabling them to evaluate and reject false information independently. In this position paper, we explore a combination of intervention methods designed to improve critical thinking in the context of online media consumption. We highlight the role of AI in supporting different stages of these interventions and present a design concept that integrates AI-driven strategies to foster critical reasoning and media literacy.

cs.HC↗

arXiv2025

Identifying Ethical Challenges in XR Implementations in the Industrial Domain: A Case of Off-Highway Machinery

Although extended reality(XR)-using technologies have started to be discussed in the industrial setting, it is becoming important to understand how to implement them ethically and privacy-preservingly. In our paper, we summarise our experience of developing XR implementations for the off-highway machinery domain by pointing to the main challenges we identified during the work. We believe that our findings can be a starting point for further discussion and future research regarding privacy and ethical challenges in industrial applications of XR.

cs.HC↗

arXiv2025

More Than Just Warnings:Exploring the Ways of Communicating Credibility Assessment on Social Media

Reducing the spread of misinformation is challenging. AI-based fact verification systems offer a promising solution by addressing the high costs and slow pace of traditional fact-checking. However, the problem of how to effectively communicate the results to users remains unsolved. Warning labels may seem an easy solution, but they fail to account for fuzzy misinformation that is not entirely fake. Additionally, users' limited attention spans and social media information should be taken into account while designing the presentation. The online experiment (n = 537) investigates the impact of sources and granularity on users' perception of information veracity and the system's usefulness and trustworthiness. Findings show that fine-grained indicators enhance nuanced opinions, information awareness, and the intention to use fact-checking systems. Source differences had minimal impact on opinions and perceptions, except for informativeness. Qualitative findings suggest the proposed indicators promote critical thinking. We discuss implications for designing concise, user-friendly AI fact-checking feedback.

cs.HC↗

arXiv2025

Community Fact-Checks Trigger Moral Outrage in Replies to Misleading Posts on Social Media

Displaying community fact-checks is a promising approach to reduce engagement with misinformation on social media. However, how users respond to misleading content emotionally after community fact-checks are displayed on posts is unclear. Here, we employ quasi-experimental methods to causally analyze changes in sentiments and (moral) emotions in replies to misleading posts following the display of community fact-checks. Our evaluation is based on a large-scale panel dataset comprising N=2,225,260 replies across 1841 source posts from X's Community Notes platform. We find that informing users about falsehoods through community fact-checks significantly increases negativity (by 7.3%), anger (by 13.2%), disgust (by 4.7%), and moral outrage (by 16.0%) in the corresponding replies. These results indicate that users perceive spreading misinformation as a violation of social norms and that those who spread misinformation should expect negative reactions once their content is debunked. We derive important implications for the design of community-based fact-checking systems.

cs.SI↗

arXiv2024

The Effects of Group Discussion and Role-playing Training on Self-efficacy, Support-seeking, and Reporting Phishing Emails: Evidence from a Mixed-design Experiment

Organizations rely on phishing interventions to enhance employees' vigilance and safe responses to phishing emails that bypass technical solutions. While various resources are available to counteract phishing, studies emphasize the need for interactive and practical training approaches. To investigate the effectiveness of such an approach, we developed and delivered two anti-phishing trainings, group discussion and role-playing, at a European university. We conducted a pre-registered experiment (N = 105), incorporating repeated measures at three time points, a control group, and three in-situ phishing tests. Both trainings enhanced employees' anti-phishing self-efficacy and support-seeking intention in within-group analyses. Only the role-playing training significantly improved support-seeking intention when compared to the control group. Participants in both trainings reported more phishing tests and demonstrated heightened vigilance to phishing attacks compared to the control group. We discuss practical implications for evaluating and improving phishing interventions and promoting safe responses to phishing threats within organizations.

cs.HC↗

arXiv2024

Impact of Voice Fidelity on Decision Making: A Potential Dark Pattern?

Manipulative design in user interfaces (conceptualized as dark patterns) has emerged as a significant impediment to the ethical design of technology and a threat to user agency and freedom of choice. While previous research focused on exploring these patterns in the context of graphical user interfaces, the impact of speech has largely been overlooked. We conducted a listening test (N = 50) to elicit participants' preferences regarding different synthetic voices that varied in terms of synthesis method (concatenative vs. neural) and prosodic qualities (speech pace and pitch variance), and then evaluated their impact in an online decision-making study (N = 101). Our results indicate a significant effect of voice qualities on the participant's choices, independently from the content of the available options. Our results also indicate that the voice's perceived engagement, ease of understanding, and domain fit directly translate to its impact on participants' behaviour in decision-making tasks.

cs.HC↗

arXiv2023

Right, No Matter Why: AI Fact-checking and AI Authority in Health-related Inquiry Settings

Previous research on expert advice-taking shows that humans exhibit two contradictory behaviors: on the one hand, people tend to overvalue their own opinions undervaluing the expert opinion, and on the other, people often defer to other people's advice even if the advice itself is rather obviously wrong. In our study, we conduct an exploratory evaluation of users' AI-advice accepting behavior when evaluating the truthfulness of a health-related statement in different "advice quality" settings. We find that even feedback that is confined to just stating that "the AI thinks that the statement is false/true" results in more than half of people moving their statement veracity assessment towards the AI suggestion. The different types of advice given influence the acceptance rates, but the sheer effect of getting a suggestion is often bigger than the suggestion-type effect.

cs.CL↗

arXiv2018

Neural Network-based exploration of construct validity for Russian version of the 10-item Big Five Inventory

This study aims to present a new method of exploring construct validity of questionnaires based on neural network. Using this test we further explore convergent validity for Russian adaptation of TIPI (Ten-Item Personality Inventory by Gosling, Rentfrow, and Swann). Due to small number of questions TIPI-RU can be used as an express-method for surveying large number of people, especially in the Internet-studies. It can be also used with other translations of the same questionnaire in the intercultural studies. The neural network test for construct validity can be used as more convenient substitute for path model.

cs.OH↗