arXiv subjects

Sarah Rajtmajer

Publications and source records attributed to Sarah Rajtmajer.

At least 37 records · Page 2Linked to original sources

arXiv2025

Can Third-parties Read Our Emotions?

Natural Language Processing tasks that aim to infer an author's private states, e.g., emotions and opinions, from their written text, typically rely on datasets annotated by third-party annotators. However, the assumption that third-party annotators can accurately capture authors' private states remains largely unexamined. In this study, we present human subjects experiments on emotion recognition tasks that directly compare third-party annotations with first-party (author-provided) emotion labels. Our findings reveal significant limitations in third-party annotations-whether provided by human annotators or large language models (LLMs)-in faithfully representing authors' private states. However, LLMs outperform human annotators nearly across the board. We further explore methods to improve third-party annotation quality. We find that demographic similarity between first-party authors and third-party human annotators enhances annotation performance. While incorporating first-party demographic information into prompts leads to a marginal but statistically significant improvement in LLMs' performance. We introduce a framework for evaluating the limitations of third-party annotations and call for refined annotation practices to accurately represent and model authors' private states.

cs.CL↗

arXiv2025

Monolingual and Multilingual Misinformation Detection for Low-Resource Languages: A Comprehensive Survey

In today's global digital landscape, misinformation transcends linguistic boundaries, posing a significant challenge for moderation systems. Most approaches to misinformation detection are monolingual, focused on high-resource languages, i.e., a handful of world languages that have benefited from substantial research investment. This survey provides a comprehensive overview of the current research on misinformation detection in low-resource languages, both in monolingual and multilingual settings. We review existing datasets, methodologies, and tools used in these domains, identifying key challenges related to: data resources, model development, cultural and linguistic context, and real-world applications. We examine emerging approaches, such as language-generalizable models and multi-modal techniques, and emphasize the need for improved data collection practices, interdisciplinary collaboration, and stronger incentives for socially responsible AI research. Our findings underscore the importance of systems capable of addressing misinformation across diverse linguistic and cultural contexts.

cs.CL↗

arXiv2025

Have LLMs Reopened the Pandora's Box of AI-Generated Fake News?

With the rise of AI-generated content spewed at scale from large language models (LLMs), genuine concerns about the spread of fake news have intensified. The perceived ability of LLMs to produce convincing fake news at scale poses new challenges for both human and automated fake news detection systems. To address this gap, this paper presents the findings from a university-level competition that aimed to explore how LLMs can be used by humans to create fake news, and to assess the ability of human annotators and AI models to detect it. A total of 110 participants used LLMs to create 252 unique fake news stories, and 84 annotators participated in the detection tasks. Our findings indicate that LLMs are ~68% more effective at detecting real news than humans. However, for fake news detection, the performance of LLMs and humans remains comparable (~60% accuracy). Additionally, we examine the impact of visual elements (e.g., pictures) in news on the accuracy of detecting fake news stories. Finally, we also examine various strategies used by fake news creators to enhance the credibility of their AI-generated content. This work highlights the increasing complexity of detecting AI-generated fake news, particularly in collaborative human-AI settings.

cs.CL↗

arXiv2025

The study of short texts in digital politics: Document aggregation for topic modeling

Statistical topic modeling is widely used in political science to study text. Researchers examine documents of varying lengths, from tweets to speeches. There is ongoing debate on how document length affects the interpretability of topic models. We investigate the effects of aggregating short documents into larger ones based on natural units that partition the corpus. In our study, we analyze one million tweets by U.S. state legislators from April 2016 to September 2020. We find that for documents aggregated at the account level, topics are more associated with individual states than when using individual tweets. This finding is replicated with Wikipedia pages aggregated by birth cities, showing how document definitions can impact topic modeling results.

cs.CL↗

arXiv2024

Perspectives from India: Opportunities and Challenges for AI Replication Prediction to Improve Confidence in Published Research

Over the past decade, a crisis of confidence in scientific literature has gained attention, particularly in the West. In response, we have seen changes in policy and practice amongst individual researchers and institutions. Greater attention is given to the transparency of workflows and the appropriate use of statistical methods. Advances in scholarly big data and machine learning have led to the development of AI-driven tools for the evaluation of published findings. In this study, we conduct 19 semi-structured interviews with Indian researchers to understand their perspectives on challenges and opportunities for AI technologies to improve confidence in published research. Our findings highlight the importance of social and cultural context for the design and deployment of AI tools for research assessment. Our work suggests that such technologies must work alongside rather than replace human research assessment mechanisms. They must be explainable and situated within well-functioning human-centered peer review processes.

cs.HC↗

arXiv2024

An Audit on the Perspectives and Challenges of Hallucinations in NLP

We audit how hallucination in large language models (LLMs) is characterized in peer-reviewed literature, using a critical examination of 103 publications across NLP research. Through the examination of the literature, we identify a lack of agreement with the term `hallucination' in the field of NLP. Additionally, to compliment our audit, we conduct a survey with 171 practitioners from the field of NLP and AI to capture varying perspectives on hallucination. Our analysis calls for the necessity of explicit definitions and frameworks outlining hallucination within NLP, highlighting potential challenges, and our survey inputs provide a thematic understanding of the influence and ramifications of hallucination in society.

cs.CL↗

arXiv2024

The Unappreciated Role of Intent in Algorithmic Moderation of Social Media Content

As social media has become a predominant mode of communication globally, the rise of abusive content threatens to undermine civil discourse. Recognizing the critical nature of this issue, a significant body of research has been dedicated to developing language models that can detect various types of online abuse, e.g., hate speech, cyberbullying. However, there exists a notable disconnect between platform policies, which often consider the author's intention as a criterion for content moderation, and the current capabilities of detection models, which typically lack efforts to capture intent. This paper examines the role of intent in content moderation systems. We review state of the art detection models and benchmark training datasets for online abuse to assess their awareness and ability to capture intent. We propose strategic changes to the design and development of automated detection and moderation systems to improve alignment with ethical and policy conceptualizations of abuse.

cs.CL↗

arXiv2024

Integrating measures of replicability into scholarly search: Challenges and opportunities

Challenges to reproducibility and replicability have gained widespread attention, driven by large replication projects with lukewarm success rates. A nascent work has emerged developing algorithms to estimate the replicability of published findings. The current study explores ways in which AI-enabled signals of confidence in research might be integrated into the literature search. We interview 17 PhD researchers about their current processes for literature search and ask them to provide feedback on a replicability estimation tool. Our findings suggest that participants tend to confuse replicability with generalizability and related concepts. Information about replicability can support researchers throughout the research design processes. However, the use of AI estimation is debatable due to the lack of explainability and transparency. The ethical implications of AI-enabled confidence assessment must be further studied before such tools could be widely accepted. We discuss implications for the design of technological tools to support scholarly activities and advance replicability.

cs.DL↗

arXiv2024

Inside the echo chamber: Linguistic underpinnings of misinformation on Twitter

Social media users drive the spread of misinformation online by sharing posts that include erroneous information or commenting on controversial topics with unsubstantiated arguments often in earnest. Work on echo chambers has suggested that users' perspectives are reinforced through repeated interactions with like-minded peers, promoted by homophily and bias in information diffusion. Building on long-standing interest in the social bases of language and linguistic underpinnings of social behavior, this work explores how conversations around misinformation are mediated through language use. We compare a number of linguistic measures, e.g., in-/out-group cues, readability, and discourse connectives, within and across topics of conversation and user communities. Our findings reveal increased presence of group identity signals and processing fluency within echo chambers during discussions of misinformation. We discuss the specific character of these broader trends across topics and examine contextual influences.

cs.SI↗

arXiv2024

Can Large Language Models Discern Evidence for Scientific Hypotheses? Case Studies in the Social Sciences

Hypothesis formulation and testing are central to empirical research. A strong hypothesis is a best guess based on existing evidence and informed by a comprehensive view of relevant literature. However, with exponential increase in the number of scientific articles published annually, manual aggregation and synthesis of evidence related to a given hypothesis is a challenge. Our work explores the ability of current large language models (LLMs) to discern evidence in support or refute of specific hypotheses based on the text of scientific abstracts. We share a novel dataset for the task of scientific hypothesis evidencing using community-driven annotations of studies in the social sciences. We compare the performance of LLMs to several state-of-the-art benchmarks and highlight opportunities for future research in this area. The dataset is available at https://github.com/Sai90000/ScientificHypothesisEvidencing.git

cs.CL↗

arXiv2024

Reproducibility, Replicability, and Transparency in Research: What 430 Professors Think in Universities across the USA and India

In the past decade, open science and science of science communities have initiated innovative efforts to address concerns about the reproducibility and replicability of published scientific research. In some respects, these efforts have been successful, yet there are still many pockets of researchers with little to no familiarity with these concerns, subsequent responses, or best practices for engaging in reproducible, replicable, and reliable scholarship. In this work, we survey 430 professors from Universities across the USA and India to understand perspectives on scientific processes and identify key points for intervention. Our findings reveal both national and disciplinary gaps in attention to reproducibility and replicability, aggravated by incentive misalignment and resource constraints. We suggest that solutions addressing scientific integrity should be culturally-centered, where definitions of culture should include both regional and domain-specific elements.

cs.HC↗

arXiv2023

Exploring Trust and Risk during Online Bartering Interactions

This paper investigates how risk influences the way people barter. We used Minecraft to create an experimental environment in which people bartered to earn a monetary bonus. Our findings reveal that subjects exhibit risk-aversion to competitive bartering environments and deliberate over their trades longer when compared to cooperative environments. These initial experiments lay groundwork for development of agents capable of strategically trading with human counterparts in different environments.

cs.GT↗

arXiv2023

Effects of Online Self-Disclosure on Social Feedback During the COVID-19 Pandemic

We investigate relationships between online self-disclosure and received social feedback during the COVID-19 crisis. We crawl a total of 2,399 posts and 29,851 associated comments from the r/COVID19_support subreddit and manually extract fine-grained personal information categories and types of social support sought from each post. We develop a BERT-based ensemble classifier to automatically identify types of support offered in users' comments. We then analyze the effect of personal information sharing and posts' topical, lexical, and sentiment markers on the acquisition of support and five interaction measures (submission scores, the number of comments, the number of unique commenters, the length and sentiments of comments). Our findings show that: 1) users were more likely to share their age, education, and location information when seeking both informational and emotional support, as opposed to pursuing either one; 2) while personal information sharing was positively correlated with receiving informational support when requested, it did not correlate with emotional support; 3) as the degree of self-disclosure increased, information support seekers obtained higher submission scores and longer comments, whereas emotional support seekers' self-disclosure resulted in lower submission scores, fewer comments, and fewer unique commenters; 4) post characteristics affecting social feedback differed significantly based on types of support sought by post authors. These results provide empirical evidence for the varying effects of self-disclosure on acquiring desired support and user involvement online during the COVID-19 pandemic. Furthermore, this work can assist support seekers hoping to enhance and prioritize specific types of social feedback.

cs.SI↗

arXiv2023

Evidence of Inter-state Coordination amongst State-backed Information Operations

Since 2018, Twitter has steadily released into the public domain content discovered on the platform and believed to be associated with information operations originating from more than a dozen state-backed organizations. Leveraging this dataset, we explore inter-state coordination amongst state-backed information operations and find evidence of intentional, strategic interaction amongst thirteen different states, separate and distinct from within-state operations. We find that coordinated, inter-state information operations attract greater engagement than baseline information operations and appear to come online in service to specific aims. We explore these ideas in depth through two case studies on the coordination between Cuba and Venezuela, and between Russia and Iran.

cs.SI↗

arXiv2023

A Study on Reproducibility and Replicability of Table Structure Recognition Methods

Concerns about reproducibility in artificial intelligence (AI) have emerged, as researchers have reported unsuccessful attempts to directly reproduce published findings in the field. Replicability, the ability to affirm a finding using the same procedures on new data, has not been well studied. In this paper, we examine both reproducibility and replicability of a corpus of 16 papers on table structure recognition (TSR), an AI task aimed at identifying cell locations of tables in digital documents. We attempt to reproduce published results using codes and datasets provided by the original authors. We then examine replicability using a dataset similar to the original as well as a new dataset, GenTSR, consisting of 386 annotated tables extracted from scientific papers. Out of 16 papers studied, we reproduce results consistent with the original in only four. Two of the four papers are identified as replicable using the similar dataset under certain IoU values. No paper is identified as replicable using the new dataset. We offer observations on the causes of irreproducibility and irreplicability. All code and data are available on Codeocean at https://codeocean.com/capsule/6680116/tree.

cs.CV↗

arXiv2023

Information Operations in Turkey: Manufacturing Resilience with Free Twitter Accounts

Following the 2016 US elections Twitter launched their Information Operations (IO) hub where they archive account activity connected to state linked information operations. In June 2020, Twitter took down and released a set of accounts linked to Turkey's ruling political party (AKP). We investigate these accounts in the aftermath of the takedown to explore whether AKP-linked operations are ongoing and to understand the strategies they use to remain resilient to disruption. We collect live accounts that appear to be part of the same network, ~30% of which have been suspended by Twitter since our collection. We create a BERT-based classifier that shows similarity between these two networks, develop a taxonomy to categorize these accounts, find direct sequel accounts between the Turkish takedown and the live accounts, and find evidence that Turkish IO actors deliberately construct their network to withstand large-scale shutdown by utilizing explicit and implicit signals of coordination. We compare our findings from the Turkish operation to Russian and Chinese IO on Twitter and find that Turkey's IO utilizes a unique group structure to remain resilient. Our work highlights the fundamental imbalance between IO actors quickly and easily creating free accounts and the social media platforms spending significant resources on detection and removal, and contributes novel findings about Turkish IO on Twitter.

cs.SI↗

arXiv2023

A prototype hybrid prediction market for estimating replicability of published work

We present a prototype hybrid prediction market and demonstrate the avenue it represents for meaningful human-AI collaboration. We build on prior work proposing artificial prediction markets as a novel machine-learning algorithm. In an artificial prediction market, trained AI agents buy and sell outcomes of future events. Classification decisions can be framed as outcomes of future events, and accordingly, the price of an asset corresponding to a given classification outcome can be taken as a proxy for the confidence of the system in that decision. By embedding human participants in these markets alongside bot traders, we can bring together insights from both. In this paper, we detail pilot studies with prototype hybrid markets for the prediction of replication study outcomes. We highlight challenges and opportunities, share insights from semi-structured interviews with hybrid market participants, and outline a vision for ongoing and future work.

cs.HC↗

arXiv2022

Artificial prediction markets present a novel opportunity for human-AI collaboration

Despite high-profile successes in the field of Artificial Intelligence, machine-driven technologies still suffer important limitations, particularly for complex tasks where creativity, planning, common sense, intuition, or learning from limited data is required. These limitations motivate effective methods for human-machine collaboration. Our work makes two primary contributions. We thoroughly experiment with an artificial prediction market model to understand the effects of market parameters on model performance for benchmark classification tasks. We then demonstrate, through simulation, the impact of exogenous agents in the market, where these exogenous agents represent primitive human behaviors. This work lays the foundation for a novel set of hybrid human-AI machine learning algorithms.

cs.IT↗