arXiv subjects

Milena Tsvetkova

Publications and source records attributed to Milena Tsvetkova.

16 recordsLinked to original sources

arXiv2026

Mapping the Reddit Bot Ecosystem: Taxonomy and Evolution

Automated agents increasingly participate in online communities, yet their population structure and roles remain poorly understood. Using a dataset of 3,389 identified bots and their full activity histories, we construct a taxonomy of bot "species" on the news aggregation and social media platform Reddit based on temporal, community, linguistic, and semantic features. Clustering analysis reveals 18 distinct bot types spanning content-specialized, behavior-driven, and infrastructural roles such as moderation and utility support. In addition, temporal analysis shows that bot numbers and activity expanded rapidly before peaking around the COVID-19 period, then started declining even before Reddit's 2023 API policy changes. However, the overall diversity of bot species has remained remarkably stable. These findings suggest that online bot populations form evolving digital ecosystems.

cs.SI↗

arXiv2026

Artificially intelligent agents in the social and behavioral sciences: A history and outlook

We review the historical development and current trends of artificially intelligent agents (agentic AI) in the social and behavioral sciences: from the first programmable computers, and social simulations soon thereafter, to today's experiments with large language models. This overview emphasizes the role of AI in the scientific process and the changes brought about, both through technological advancements and the broader evolution of science from around 1950 to the present. Some of the specific points we cover include: the challenges of presenting the first social simulation studies to a world unaware of computers, the rise of social systems science, intelligent game theoretic agents, the age of big data and the epistemic upheaval in its wake, and the current enthusiasm around applications of generative AI, and many other topics. A pervasive theme is how deeply entwined we are with the technologies we use to understand ourselves.

cs.AI↗

arXiv2026

A field experiment of social influence and behavioral contagion with bots on Reddit

Recent advances in AI have heightened scholars' and policy makers' concern with social influence and behavioral contagion in online communities. We conduct a field experiment on Reddit to investigate the extent to which online users are susceptible to positive behavioral stimuli from other users and artificial agents. We let apparent human and bot accounts give symbolic awards to users with one of four rationales: praising the recipient's logical argument, emotional sensitivity, or moral integrity, or explaining that the award resulted from a random draw in a lottery. We evaluate how the different rationales for the award affect the recipients' subsequent behavior on the platform in terms of volume, impact, and content, as well as the further behavioral contagion to other users. We find that awards do not increase user activity and downstream impact, and awards from bots with the lottery rationale can in fact reduce them. Nevertheless, awards encourage direct communication between users. These findings highlight the possible resilience of online users to simple behavioral manipulation from platform algorithms and artificial agents, but not necessarily to more sophisticated schemes that simulate human conversation. Transparently labeling automated agents remains essential for ethical and effective platform governance.

cs.SI↗

arXiv2025

High Socioeconomic Status is Associated with Diverse Consumption across Brands and Price Levels

Consumption practices are determined by a combination of economic, social, and cultural forces. We posit that lower economic constraints leave more room to diversify consumption along cultural and social aspects in the form of omnivorous or lifestyle-based niche consumption. We provide empirical evidence for this diversity hypothesis by analysing millions of mobile-tracked visits from thousands of Census Block Groups to thousands of stores in New York State. The results show that high income is significantly associated with diverse consumption across brands and price levels. The associations between diversity and income persist but are less prominent for necessity-based consumption and for the densely populated and demographically diverse New York City. The associations replicate for education as an alternative measure of socioeconomic status and for the state of Texas. We further illustrate that the associations cannot be explained by simple geographic constraints, including the neighbourhoods' demographic diversity, the residents' geographic mobility and the stores' local availability, so deeper social and cultural factors must be at play.

econ.GN↗

arXiv2025

A new sociology of humans and machines

From fake social media accounts and generative artificial intelligence chatbots to trading algorithms and self-driving vehicles, robots, bots and algorithms are proliferating and permeating our communication channels, social interactions, economic transactions and transportation arteries. Networks of multiple interdependent and interacting humans and intelligent machines constitute complex social systems for which the collective outcomes cannot be deduced from either human or machine behaviour alone. Under this paradigm, we review recent research and identify general dynamics and patterns in situations of competition, coordination, cooperation, contagion and collective decision-making, with context-rich examples from high-frequency trading markets, a social media platform, an open collaboration community and a discussion forum. To ensure more robust and resilient human-machine communities, we require a new sociology of humans and machines. Researchers should study these communities using complex system methods; engineers should explicitly design artificial intelligence for human-machine and machine-machine interactions; and regulators should govern the ecological diversity and social co-development of humans and machines.

cs.SI↗

arXiv2023

Individual and gender inequality in computer science: A career study of cohorts from 1970 to 2000

Inequality prevails in science. Individual inequality means that most perish quickly and only a few are successful, while gender inequality implies that there are differences in achievements for women and men. Using large-scale bibliographic data and following a computational approach, we study the evolution of individual and gender inequality for cohorts from 1970 to 2000 in the whole field of computer science as it grows and becomes a team-based science. We find that individual inequality in productivity (publications) increases over a scholar's career but is historically invariant, while individual inequality in impact (citations), albeit larger, is stable across cohorts and careers. Gender inequality prevails regarding productivity, but there is no evidence for differences in impact. The Matthew Effect is shown to accumulate advantages to early achievements and to become stronger over the decades, indicating the rise of a "publish or perish" imperative. Only some authors manage to reap the benefits that publishing in teams promises. The Matthew Effect then amplifies initial differences and propagates the gender gap. Women continue to fall behind because they continue to be at a higher risk of dropping out for reasons that have nothing to do with early-career achievements or social support. Our findings suggest that mentoring programs for women to improve their social-networking skills can help to reduce gender inequality.

cs.CY↗

arXiv2023

A Method for Estimating Individual Socioeconomic Status of Twitter Users

The rise of social media has opened countless opportunities to explore social science questions with new data and methods. However, research on socioeconomic inequality remains constrained by limited individual-level socioeconomic status (SES) measures in digital trace data. Following Bourdieu, we argue that the commercial and entertainment accounts Twitter users follow reflect their economic and cultural capital. Adapting a political science method for inferring political ideology, we use correspondence analysis to estimate the SES of 3,482,652 Twitter users who follow the accounts of 339 brands in the United States. We validate our estimates with data from the Facebook Marketing API, self-reported job titles on users' Twitter profiles, and a small survey sample. The results show reasonable correlations with the standard proxies for SES, alongside much weaker or non-significant correlations with other demographic variables. The proposed method opens new opportunities for innovative social research on inequality on Twitter and similar online platforms.

cs.SI↗

arXiv2023

The cost of coordination can exceed the benefit of collaboration in performing complex tasks

Humans and other intelligent agents often rely on collective decision making based on an intuition that groups outperform individuals. However, at present, we lack a complete theoretical understanding of when groups perform better. Here we examine performance in collective decision-making in the context of a real-world citizen science task environment in which individuals with manipulated differences in task-relevant training collaborated. We find 1) dyads gradually improve in performance but do not experience a collective benefit compared to individuals in most situations; 2) the cost of coordination to efficiency and speed that results when switching to a dyadic context after training individually is consistently larger than the leverage of having a partner, even if they are expertly trained in that task; and 3) on the most complex tasks having an additional expert in the dyad who is adequately trained improves accuracy. These findings highlight that the extent of training received by an individual, the complexity of the task at hand, and the desired performance indicator are all critical factors that need to be accounted for when weighing up the benefits of collective decision-making.

cs.SI↗

arXiv2022

Cheating in online gaming spreads through observation and victimization

Antisocial behavior can be contagious, spreading from individual to individual and rippling through social networks. Moreover, it can spread not only through third-party influence from observation, just like innovations or individual behavior do, but also through direct experience, via "pay-it-forward" retaliation. Here, we distinguish between the effects of observation and victimization for the contagion of antisocial behavior by analyzing large-scale digital-trace data. We study the spread of cheating in more than a million matches of an online multiplayer first-person shooter game, in which up to 100 players compete individually or in teams against strangers. We identify event sequences in which a player who observes or is killed by a certain number of cheaters starts cheating, and evaluate the extent to which these sequences would appear if we preserve the team and interaction structure but assume alternative gameplay scenarios. The results reveal that social contagion is only likely to exist for those who both observe and experience cheating, suggesting that third-party influence and "pay-it-forward" reciprocity interact positively. In addition, the effect is present only for those who both observe and experience more than once, suggesting that cheating is more likely to spread after repeated or multi-source exposure. Approaching online games as models of social systems, we use the findings to discuss strategies for targeted interventions to stem the spread of cheating and antisocial behavior more generally in online communities, schools, organizations, and sports.

cs.SI↗

arXiv2017

Even Good Bots Fight: The Case of Wikipedia

In recent years, there has been a huge increase in the number of bots online, varying from Web crawlers for search engines, to chatbots for online customer service, spambots on social media, and content-editing bots in online collaboration communities. The online world has turned into an ecosystem of bots. However, our knowledge of how these automated agents are interacting with each other is rather poor. Bots are predictable automatons that do not have the capacity for emotions, meaning-making, creativity, and sociality and it is hence natural to expect interactions between bots to be relatively predictable and uneventful. In this article, we analyze the interactions between bots that edit articles on Wikipedia. We track the extent to which bots undid each other's edits over the period 2001-2010, model how pairs of bots interact over time, and identify different types of interaction trajectories. We find that, although Wikipedia bots are intended to support the encyclopedia, they often undo each other's edits and these sterile "fights" may sometimes continue for years. Unlike humans on Wikipedia, bots' interactions tend to occur over longer periods of time and to be more reciprocated. Yet, just like humans, bots in different cultural environments may behave differently. Our research suggests that even relatively "dumb" bots may give rise to complex interactions, and this carries important implications for Artificial Intelligence research. Understanding what affects bot-bot interactions is crucial for managing social media well, providing adequate cyber-security, and designing well functioning autonomous vehicles.

cs.SI↗

arXiv2017

Understanding Human-Machine Networks: A Cross-Disciplinary Survey

In the current hyper-connected era, modern Information and Communication Technology systems form sophisticated networks where not only do people interact with other people, but also machines take an increasingly visible and participatory role. Such human-machine networks (HMNs) are embedded in the daily lives of people, both for personal and professional use. They can have a significant impact by producing synergy and innovations. The challenge in designing successful HMNs is that they cannot be developed and implemented in the same manner as networks of machines nodes alone, nor following a wholly human-centric view of the network. The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. Extending the previous theoretical concepts of socio-technical systems, actor-network theory, cyber-physical-social systems, and social machines, we concentrate on the interactions among humans and between humans and machines. We identify eight types of HMNs: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. We systematically select literature on each of these types and review it with a focus on implications for designing HMNs. Moreover, we discuss risks associated with HMNs and identify emerging design and development trends.

cs.SI↗

arXiv2016

Dynamics of Disagreement: Large-Scale Temporal Network Analysis Reveals Negative Interactions in Online Collaboration

Disagreement and conflict are a fact of social life and considerably affect our well-being and productivity. Such negative interactions are rarely explicitly declared and recorded and this makes them hard for scientists to study. We overcome this challenge by investigating the patterns in the timing and configuration of contributions to a large online collaboration community. We analyze sequences of reverts of contributions to Wikipedia, the largest online encyclopedia, and investigate how often and how fast they occur compared to a null model that randomizes the order of actions to remove any systematic clustering. We find evidence that individuals systematically attack the same person and attack back their attacker; both of these interactions occur at a faster response rate than expected. We also establish that individuals come to defend an attack victim but we do not find evidence that attack victims "pay it forward" or that attackers collude to attack the same individual. We further find that high-status contributors are more likely to attack many others serially, status equals are more likely to revenge attacks back, while attacks by lower-status contributors trigger attacks forward; yet, it is the lower-status contributors who also come forward to defend third parties. The method we use can be applied to other large-scale temporal communication and collaboration networks to identify the existence of negative social interactions and other social processes.

cs.SI↗

arXiv2016

An Experimental Study of Segregation Mechanisms

Segregation is widespread in all realms of human society. Several influential studies have argued that intolerance is not a prerequisite for a segregated society, and that segregation can arise even when people generally prefer diversity. We investigated this paradox experimentally, by letting groups of high-school students play four different real-time interactive games. Incentives for neighbor similarity produced segregation, but incentives for neighbor dissimilarity and neighborhood diversity prevented it. The participants continued to move while their game scores were below optimal, but their individual moves did not consistently take them to the best alternative position. These small differences between human and simulated agents produced different segregation patterns than previously predicted, thus challenging conclusions about segregation arising from these models.

cs.SI↗

arXiv2016

Dynamics and Biases of Online Attention: The Case of Aircraft Crashes

The Internet not only has changed the dynamics of our collective attention, but also through the transactional log of online activities, provides us with the opportunity to study attention dynamics at scale. In this paper, we particularly study attention to aircraft incidents and accidents using Wikipedia transactional data in two different language editions, English and Spanish. We study both the editorial activities on and the viewership of the articles about airline crashes. We analyse how the level of attention is influenced by different parameters such as number of deaths, airline region, and event locale and date. We find evidence that the attention given by Wikipedia editors to pre-Wikipedia aircraft incidents and accidents depends on the region of the airline for both English and Spanish editions. North American airline companies receive more prompt coverage in English Wikipedia. We also observe that the attention given by Wikipedia visitors is influenced by the airline region but only for events with high number of deaths. Finally we show that the rate and time span of the decay of attention is independent of the number of deaths and a fast decay within about a week seems to be universal. We discuss the implications of these findings in the context of attention bias.

physics.soc-ph↗

arXiv2016

Memory Remains: Understanding Collective Memory in the Digital Age

Recently developed information communication technologies, particularly the Internet, have affected how we, both as individuals and as a society, create, store, and recall information. Internet also provides us with a great opportunity to study memory using transactional large scale data, in a quantitative framework similar to the practice in statistical physics. In this project, we make use of online data by analysing viewership statistics of Wikipedia articles on aircraft crashes. We study the relation between recent events and past events and particularly focus on understanding memory triggering patterns. We devise a quantitative model that explains the flow of viewership from a current event to past events based on similarity in time, geography, topic, and the hyperlink structure of Wikipedia articles. We show that on average the secondary flow of attention to past events generated by such remembering processes is larger than the primary attention flow to the current event. We are the first to report these cascading effects.

physics.soc-ph↗

arXiv2016

Human-Machine Networks: Towards a Typology and Profiling Framework

In this paper we outline an initial typology and framework for the purpose of profiling human-machine networks, that is, collective structures where humans and machines interact to produce synergistic effects. Profiling a human-machine network along the dimensions of the typology is intended to facilitate access to relevant design knowledge and experience. In this way the profiling of an envisioned or existing human-machine network will both facilitate relevant design discussions and, more importantly, serve to identify the network type. We present experiences and results from two case trials: a crisis management system and a peer-to-peer reselling network. Based on the lessons learnt from the case trials we suggest potential benefits and challenges, and point out needed future work.

cs.HC↗