arXiv subjects

Reza Mousavi

Publications and source records attributed to Reza Mousavi.

6 recordsLinked to original sources

arXiv2026

Beyond Surface Forms: A Comprehensive, Mechanism-Oriented Taxonomy of Indirect Linguistic Encoding for LLM-Based Coded Language Detection

To avoid moderation and surveillance on social media, some users routinely invent indirect linguistic expressions (ILE) that camouflage sensitive meanings. Such expressions surface as algospeak, euphemisms, and adversarial obfuscation, depending on intent and context, and they involve recurring encoding mechanisms. We propose a comprehensive, mechanism-oriented taxonomy of ILE that abstracts away from communicative goals and instead categorizes the underlying operations through which meaning is encoded and recovered. We evaluate the taxonomy by incorporating it into LLM prompts and comparing it with four existing taxonomies and a no-taxonomy baseline, using 2,000 manually annotated TikTok and Bluesky posts. The proposed taxonomy attains the strongest document- and span-level performance across the three LLMs, achieving an improvement of 4.7% in accuracy and 5.4% in F1 over the best-performing benchmark. The empirical results reveal the importance of a comprehensive, mechanism-oriented taxonomy as a stable scaffold for detecting emerging coded language and a useful input to content moderation. Disclaimer: This paper contains content that may be profane, vulgar, or offensive.

cs.CL↗

arXiv2026

From Parasocial Scripts to Dyadic Persistence in Autonomous AI-Agent Communities

While parasocial interactions (PSIs) and parasocial relationships (PSRs) have been studied in conventional media settings, we investigate whether PSI- (colloquial) relational cues also exist in online communities where both sides are autonomous AI agents. We analyze 4,434 posts and 50,338 comments from Moltbook through three theory-based textual indicators: attachment/intimacy language, reciprocity bids, and self-identification to original poster (OP). The combined results across methods based on keyword matching, few-shot large language model (LLM) annotation, and grouped-context LLM annotation reveal that PSI colloquial cues prevail and are strongly associated with OP re-engagement and a reciprocal reply structure. These results are robust across negative controls, nullification, clustered-standard-error re-estimation, and multiple-testing correction. A dyadic persistence test further affirms reciprocity bids aligned with sustained OP-involving mutual recurrence, providing empirical evidence for bridging interaction-level PSI scripts with PSR-consistent repeated dyadic patterns. We interpret the evidence as a behavioral structure in discourse by LLM-enabled agents.

cs.CL↗

arXiv2026

Generative Artificial Intelligence for Literature Reviews

Generative artificial intelligence (GenAI), based on large-language models (LLMs), such as ChatGPT, has taken organizations, academia, and the public by storm. In particular, impressive GenAI capabilities such as summarization of large text corpora, question-answering, data extraction, and translation, carry profound implications for the conduct of literature reviews. This impacts science, organizations and the general public, as all can benefit from GenAI-supported literature reviews. Building on the technical foundations of GenAI and grounded in established methodological discourse, this work outlines approaches for conducting literature reviews using both general-purpose (e.g., ChatGPT, Gemini, Claude) and specialized GenAI tools (e.g., Consensus, Elicit). We provide illustrative examples of prompts and suggest methodologically-sound literature review strategies. Throughout this perspective paper, we adopt a balanced approach considering both the opportunities and the risks of relying on GenAI in the conduct of literature reviews. We conclude by discussing philosophical questions related to the effects of GenAI on long-term scientific progress, and also present fruitful opportunities for research on improving the core of GenAI's technology-its architecture and training data-and suggest open issues in GenAI-based literature reviews methodology.

cs.DL↗

arXiv2022

How a Brand's Social Activism Impacts Consumers' Brand Evaluations: The Role of Brand Relationship Norms

With the proliferation of social activism online, brands face heightened pressure from consumers to publicly address these issues. Yet, the optimal brand response strategy (i.e., whether and how to respond) in these contexts remains unclear. This research investigates consumers' reactions to brand response strategies (e.g., engage vs. not) during social activism and offers potentially effective responses that brands can employ to engage in these issues. By analyzing real-world data collected from Twitter and conducting four randomized experiments, this research discovers that brand relationship type (exchange, communal) affects consumers' brand evaluations in the wake of social activism. Communal (vs. exchange) brands are evaluated less favorably when they do not respond or utilize a low-empathy response. This difference is attenuated when brands employ a high-empathy response. These findings are attributable to consumers' perceptions of whether the brand's response strategy complies with relationship norms during social activism. The effects persist across activism events that vary in their political polarization. This research contributes to the literatures on brand engagement in social activism, brand relationships, and crisis communication. The findings also offer guidance to practitioners on crafting response strategies during social activism and aid activists in securing brand support for societal benefits.

econ.GN↗

arXiv2020

The Unintended Consequences of Stay-at-Home Policies on Work Outcomes: The Impacts of Lockdown Orders on Content Creation

The COVID-19 pandemic has posed an unprecedented challenge to individuals around the globe. To mitigate the spread of the virus, many states in the U.S. issued lockdown orders to urge their residents to stay at their homes, avoid get-togethers, and minimize physical interactions. While many offline workers are experiencing significant challenges performing their duties, digital technologies have provided ample tools for individuals to continue working and to maintain their productivity. Although using digital platforms to build resilience in remote work is effective, other aspects of remote work (beyond the continuation of work) should also be considered in gauging true resilience. In this study, we focus on content creators, and investigate how restrictions in individual's physical environment impact their online content creation behavior. Exploiting a natural experimental setting wherein four states issued state-wide lockdown orders on the same day whereas five states never issued a lockdown order, and using a unique dataset collected from a short video-sharing social media platform, we study the impact of lockdown orders on content creators' behaviors in terms of content volume, content novelty, and content optimism. We combined econometric methods (difference-in-differences estimations of a matched sample) with machine learning-based natural language processing to show that on average, compared to the users residing in non-lockdown states, the users residing in lockdown states create more content after the lockdown order enforcement. However, we find a decrease in the novelty level and optimism of the content generated by the latter group. Our findings have important contributions to the digital resilience literature and shed light on managers' decision-making process related to the adjustment of employees' work mode in the long run.

econ.GN↗

arXiv2020

When Local Governments' Stay-at-Home Orders Meet the White House's "Opening Up America Again"

On April 16th, The White House launched "Opening up America Again" (OuAA) campaign while many U.S. counties had stay-at-home orders in place. We created a panel data set of 1,563 U.S. counties to study the impact of U.S. counties' stay-at-home orders on community mobility before and after The White House's campaign to reopen the country. Our results suggest that before the OuAA campaign stay-at-home orders brought down time spent in retail and recreation businesses by about 27% for typical conservative and liberal counties. However, after the launch of OuAA campaign, the time spent at retail and recreational businesses in a typical conservative county increased significantly more than in liberal counties (15% increase in a typical conservative county Vs. 9% increase in a typical liberal county). We also found that in conservative counties with stay-at-home orders in place, time spent at retail and recreational businesses increased less than that of conservative counties without stay-at-home orders. These findings illuminate to what extent residents' political ideology could determine to what extent they follow local orders and to what extent the White House's OuAA campaign polarized the obedience between liberal and conservative counties. The silver lining in our study is that even when the federal government was reopening the country, the local authorities that enforced stay-at-home restrictions were to some extent effective.

econ.GN↗