arXiv subjects

Jeremy Blackburn

Publications and source records attributed to Jeremy Blackburn.

At least 19 recordsLinked to original sources

arXiv2026

From Woofs to Words: Towards Intelligent Robotic Guide Dogs with Verbal Communication

Assistive robotics is an important subarea of robotics that focuses on the well-being of people with disabilities. A robotic guide dog is an assistive quadruped robot that helps visually impaired people in obstacle avoidance and navigation. Enabling language capabilities for robotic guide dogs goes beyond naively adding an existing dialog system onto a mobile robot. The novel challenges include grounding language in the dynamically changing environment and improving spatial awareness for the human handler. To address those challenges, we develop a novel dialog system for robotic guide dogs that uses LLMs to verbalize both navigational plans and scenes. The goal is to enable verbal communication for collaborative decision-making within the handler-robot team. In experiments, we conducted a human study to evaluate different verbalization strategies and a simulation study to assess the efficiency and accuracy in navigation tasks.

cs.RO↗

arXiv2025

Vision Language Models Can Parse Floor Plan Maps

Vision language models (VLMs) can simultaneously reason about images and texts to tackle many tasks, from visual question answering to image captioning. This paper focuses on map parsing, a novel task that is unexplored within the VLM context and particularly useful to mobile robots. Map parsing requires understanding not only the labels but also the geometric configurations of a map, i.e., what areas are like and how they are connected. To evaluate the performance of VLMs on map parsing, we prompt VLMs with floor plan maps to generate task plans for complex indoor navigation. Our results demonstrate the remarkable capability of VLMs in map parsing, with a success rate of 0.96 in tasks requiring a sequence of nine navigation actions, e.g., approaching and going through doors. Other than intuitive observations, e.g., VLMs do better in smaller maps and simpler navigation tasks, there was a very interesting observation that its performance drops in large open areas. We provide practical suggestions to address such challenges as validated by our experimental results. Webpage: https://sites.google.com/view/vlm-floorplan/

cs.RO↗

arXiv2025

Evaluating Large Language Models for Detecting Antisemitism

Detecting hateful content is a challenging and important problem. Automated tools, like machine-learning models, can help, but they require continuous training to adapt to the ever-changing landscape of social media. In this work, we evaluate eight open-source LLMs' capability to detect antisemitic content, specifically leveraging in-context definition. We also study how LLMs understand and explain their decisions given a moderation policy as a guideline. First, we explore various prompting techniques and design a new CoT-like prompt, Guided-CoT, and find that injecting domain-specific thoughts increases performance and utility. Guided-CoT handles the in-context policy well, improving performance and utility by reducing refusals across all evaluated models, regardless of decoding configuration, model size, or reasoning capability. Notably, Llama 3.1 70B outperforms fine-tuned GPT-3.5. Additionally, we examine LLM errors and introduce metrics to quantify semantic divergence in model-generated rationales, revealing notable differences and paradoxical behaviors among LLMs. Our experiments highlight the differences observed across LLMs' utility, explainability, and reliability. Code and resources available at: https://github.com/idramalab/quantify-llm-explanations

cs.CL↗

arXiv2025

Exploring Left-Wing Extremism on the Decentralized Web: An Analysis of Lemmygrad.ml

This study investigates the presence of left-wing extremism on the Lemmygrad.ml instance of the decentralized social media platform Lemmy, from its launch in 2019 up to a month after the bans of the subreddits r/GenZedong and r/GenZhou. We conduct a temporal analysis on Lemmygrad.ml's user activity, with also measuring the degree of highly abusive or hateful content. Furthermore, we explore the content of their posts using a transformer-based topic modeling approach. Our findings reveal a substantial increase in user activity and toxicity levels following the migration of these subreddits to Lemmygrad.ml. We also identify posts that support authoritarian regimes, endorse the Russian invasion of Ukraine, and feature anti-Zionist and antisemitic content. Overall, our findings contribute to a more nuanced understanding of political extremism within decentralized social networks and emphasize the necessity of analyzing both ends of the political spectrum in research.

cs.SI↗

arXiv2025

On the Eligibility of LLMs for Counterfactual Reasoning: A Decompositional Study

Counterfactual reasoning has emerged as a crucial technique for generalizing the reasoning capabilities of large language models (LLMs). By generating and analyzing counterfactual scenarios, researchers can assess the adaptability and reliability of model decision-making. Although prior work has shown that LLMs often struggle with counterfactual reasoning, it remains unclear which factors most significantly impede their performance across different tasks and modalities. In this paper, we propose a decompositional strategy that breaks down the counterfactual generation from causality construction to the reasoning over counterfactual interventions. To support decompositional analysis, we investigate \ntask datasets spanning diverse tasks, including natural language understanding, mathematics, programming, and vision-language tasks. Through extensive evaluations, we characterize LLM behavior across each decompositional stage and identify how modality type and intermediate reasoning influence performance. By establishing a structured framework for analyzing counterfactual reasoning, this work contributes to the development of more reliable LLM-based reasoning systems and informs future elicitation strategies.

cs.AI↗

arXiv2025

Evolving Hate Speech Online: An Adaptive Framework for Detection and Mitigation

The proliferation of social media platforms has led to an increase in the spread of hate speech, particularly targeting vulnerable communities. Unfortunately, existing methods for automatically identifying and blocking toxic language rely on pre-constructed lexicons, making them reactive rather than adaptive. As such, these approaches become less effective over time, especially when new communities are targeted with slurs not included in the original datasets. To address this issue, we present an adaptive approach that uses word embeddings to update lexicons and develop a hybrid model that adjusts to emerging slurs and new linguistic patterns. This approach can effectively detect toxic language, including intentional spelling mistakes employed by aggressors to avoid detection. Our hybrid model, which combines BERT with lexicon-based techniques, achieves an accuracy of 95% for most state-of-the-art datasets. Our work has significant implications for creating safer online environments by improving the detection of toxic content and proactively updating the lexicon. Content Warning: This paper contains examples of hate speech that may be triggering.

cs.CL↗

arXiv2024

Exploring Climate Change Discourse: Measurements and Analysis of Reddit Data

Social media is very popular for facilitating conversations about important topics and bringing forth insights and issues related to these topics. Reddit serves as a platform that fosters social interactions and hosts engaging discussions on a wide array of topics, thus forming narratives around these topics. One such topic is climate change. There are extensive discussions on Reddit about climate change, indicating high interest in its various aspects. In this paper, we explore 11 subreddits that discuss climate change for the duration of 2014 to 2022 and conduct a data-driven analysis of the posts on these subreddits. We present a basic characterization of the data and show the distribution of the posts and authors across our dataset for all years. Additionally, we analyze user engagement metrics like scores for the posts and how they change over time. We also offer insights into the topics of discussion across the subreddits, followed by entities referenced throughout the dataset.

cs.SI↗

arXiv2024

A Data-Driven Analysis of the Sovereign Citizens Movement on Telegram

Online communities of known extremist groups like the alt-right and QAnon have been well explored in past work. However, we find that an extremist group called Sovereign Citizens is relatively unexplored despite its existence since the 1970s. Their main belief is delegitimizing the established government with a tactic called paper terrorism, clogging courts with pseudolegal claims. In recent years, their activities have escalated to threats like forcefully claiming property ownership and participating in the Capitol Riot. This paper aims to shed light on Sovereign Citizens' online activities by examining two Telegram channels, each belonging to an identified Sovereign Citizen individual. We collect over 888K text messages and apply NLP techniques. We find that the two channels differ in the topics they discussed, demonstrating different focuses. Further, the two channels exhibit less toxic content compared to other extremist groups like QAnon. Finally, we find indications of overlapping beliefs between the two channels and QAnon, suggesting a merging or complementing of beliefs.

cs.SI↗

arXiv2024

PIXELMOD: Improving Soft Moderation of Visual Misleading Information on Twitter

Images are a powerful and immediate vehicle to carry misleading or outright false messages, yet identifying image-based misinformation at scale poses unique challenges. In this paper, we present PIXELMOD, a system that leverages perceptual hashes, vector databases, and optical character recognition (OCR) to efficiently identify images that are candidates to receive soft moderation labels on Twitter. We show that PIXELMOD outperforms existing image similarity approaches when applied to soft moderation, with negligible performance overhead. We then test PIXELMOD on a dataset of tweets surrounding the 2020 US Presidential Election, and find that it is able to identify visually misleading images that are candidates for soft moderation with 0.99% false detection and 2.06% false negatives.

cs.CV↗

arXiv2024

Unraveling the Web of Disinformation: Exploring the Larger Context of State-Sponsored Influence Campaigns on Twitter

Social media platforms offer unprecedented opportunities for connectivity and exchange of ideas; however, they also serve as fertile grounds for the dissemination of disinformation. Over the years, there has been a rise in state-sponsored campaigns aiming to spread disinformation and sway public opinion on sensitive topics through designated accounts, known as troll accounts. Past works on detecting accounts belonging to state-backed operations focus on a single campaign. While campaign-specific detection techniques are easier to build, there is no work done on developing systems that are campaign-agnostic and offer generalized detection of troll accounts unaffected by the biases of the specific campaign they belong to. In this paper, we identify several strategies adopted across different state actors and present a system that leverages them to detect accounts from previously unseen campaigns. We study 19 state-sponsored disinformation campaigns that took place on Twitter, originating from various countries. The strategies include sending automated messages through popular scheduling services, retweeting and sharing selective content and using fake versions of verified applications for pushing content. By translating these traits into a feature set, we build a machine learning-based classifier that can correctly identify up to 94% of accounts from unseen campaigns. Additionally, we run our system in the wild and find more accounts that could potentially belong to state-backed operations. We also present case studies to highlight the similarity between the accounts found by our system and those identified by Twitter.

cs.CY↗

arXiv2024

Podcast Outcasts: Understanding Rumble's Podcast Dynamics

Podcasting on Rumble, an alternative video-sharing platform, attracts controversial figures known for spreading divisive and often misleading content, which sharply contrasts with YouTube's more regulated environment. Motivated by the growing impact of podcasts on political discourse, as seen with figures like Joe Rogan and Andrew Tate, this paper explores the political biases and content strategies used by these platforms. In this paper, we conduct a comprehensive analysis of over 13K podcast videos from both YouTube and Rumble, focusing on their political content and the dynamics of their audiences. Using advanced speech-to-text transcription, topic modeling, and contrastive learning techniques, we explore three critical aspects: the presence of political bias in podcast channels, the nature of content that drives podcast views, and the usage of visual elements in these podcasts. Our findings reveal a distinct right-wing orientation in Rumble's podcasts, contrasting with YouTube's more diverse and apolitical content.

cs.SI↗

arXiv2024

TUBERAIDER: Attributing Coordinated Hate Attacks on YouTube Videos to their Source Communities

Alas, coordinated hate attacks, or raids, are becoming increasingly common online. In a nutshell, these are perpetrated by a group of aggressors who organize and coordinate operations on a platform (e.g., 4chan) to target victims on another community (e.g., YouTube). In this paper, we focus on attributing raids to their source community, paving the way for moderation approaches that take the context (and potentially the motivation) of an attack into consideration. We present TUBERAIDER, an attribution system achieving over 75% accuracy in detecting and attributing coordinated hate attacks on YouTube videos. We instantiate it using links to YouTube videos shared on 4chan's /pol/ board, r/The_Donald, and 16 Incels-related subreddits. We use a peak detector to identify a rise in the comment activity of a YouTube video, which signals that an attack may be occurring. We then train a machine learning classifier based on the community language (i.e., TF-IDF scores of relevant keywords) to perform the attribution. We test TUBERAIDER in the wild and present a few case studies of actual aggression attacks identified by it to showcase its effectiveness.

cs.SI↗

arXiv2024

"Here's Your Evidence": False Consensus in Public Twitter Discussions of COVID-19 Science

The COVID-19 pandemic brought about an extraordinary rate of scientific papers on the topic that were discussed among the general public, although often in biased or misinformed ways. In this paper, we present a mixed-methods analysis aimed at examining whether public discussions were commensurate with the scientific consensus on several COVID-19 issues. We estimate scientific consensus based on samples of abstracts from preprint servers and compare against the volume of public discussions on Twitter mentioning these papers. We find that anti-consensus posts and users, though overall less numerous than pro-consensus ones, are vastly over-represented on Twitter, thus producing a false consensus effect. This transpires with favorable papers being disproportionately amplified, along with an influx of new anti-consensus user sign-ups. Finally, our content analysis highlights that anti-consensus users misrepresent scientific findings or question scientists' integrity in their efforts to substantiate their claims.

cs.CY↗

arXiv2024

iDRAMA-Scored-2024: A Dataset of the Scored Social Media Platform from 2020 to 2023

Online web communities often face bans for violating platform policies, encouraging their migration to alternative platforms. This migration, however, can result in increased toxicity and unforeseen consequences on the new platform. In recent years, researchers have collected data from many alternative platforms, indicating coordinated efforts leading to offline events, conspiracy movements, hate speech propagation, and harassment. Thus, it becomes crucial to characterize and understand these alternative platforms. To advance research in this direction, we collect and release a large-scale dataset from Scored -- an alternative Reddit platform that sheltered banned fringe communities, for example, c/TheDonald (a prominent right-wing community) and c/GreatAwakening (a conspiratorial community). Over four years, we collected approximately 57M posts from Scored, with at least 58 communities identified as migrating from Reddit and over 950 communities created since the platform's inception. Furthermore, we provide sentence embeddings of all posts in our dataset, generated through a state-of-the-art model, to further advance the field in characterizing the discussions within these communities. We aim to provide these resources to facilitate their investigations without the need for extensive data collection and processing efforts.

cs.SI↗

arXiv2024

Gun Culture in Fringe Social Media

The increasing frequency of mass shootings in the United States has, unfortunately, become a norm. While the issue of gun control in the US involves complex legal concerns, there are also societal issues at play. One such social issue is so-called "gun culture," i.e., a general set of beliefs and actions related to gun ownership. However relatively little is known about gun culture, and even less is known when it comes to fringe online communities. This is especially worrying considering the aforementioned rise in mass shootings and numerous instances of shooters being radicalized online. To address this gap, we explore gun culture on /k/, 4chan's weapons board. More specifically, using a variety of quantitative techniques, we examine over 4M posts on /k/ and position their discussion within the larger body of theoretical understanding of gun culture. Among other things, our findings suggest that gun culture on /k/ covers a relatively diverse set of topics (with a particular focus on legal discussion), some of which are signals of fetishism.

cs.SI↗

arXiv2024

Spin-Resolved Topology and Partial Axion Angles in Three-Dimensional Insulators

Symmetry-protected topological crystalline insulators (TCIs) have primarily been characterized by their gapless boundary states. However, in time-reversal- ($\mathcal{T}$-) invariant (helical) 3D TCI$\unicode{x2014}$termed higher-order TCIs (HOTIs)$\unicode{x2014}$the boundary signatures can manifest as a sample-dependent network of 1D hinge states. We here introduce nested spin-resolved Wilson loops and layer constructions as tools to characterize the intrinsic bulk topological properties of spinful 3D insulators. We discover that helical HOTIs realize one of three spin-resolved phases with distinct responses that are quantitatively robust to large deformations of the bulk spin-orbital texture: 3D quantum spin Hall insulators (QSHIs), "spin-Weyl" semimetals, and $\mathcal{T}$-doubled axion insulator (T-DAXI) states with nontrivial partial axion angles indicative of a 3D spin-magnetoelectric bulk response and half-quantized 2D TI surface states originating from a partial parity anomaly. Using ab-initio calculations, we demonstrate that $β$-MoTe$_2$ realizes a spin-Weyl state and that $α$-BiBr hosts both 3D QSHI and T-DAXI regimes.

cond-mat.mes-hall↗

arXiv2023

From HODL to MOON: Understanding Community Evolution, Emotional Dynamics, and Price Interplay in the Cryptocurrency Ecosystem

This paper presents a large-scale analysis of the cryptocurrency community on Reddit, shedding light on the intricate relationship between the evolution of their activity, emotional dynamics, and price movements. We analyze over 130M posts on 122 cryptocurrency-related subreddits using temporal analysis, statistical modeling, and emotion detection. While /r/CryptoCurrency and /r/dogecoin are the most active subreddits, we find an overall surge in cryptocurrency-related activity in 2021, followed by a sharp decline. We also uncover a strong relationship in terms of cross-correlation between online activity and the price of various coins, with the changes in the number of posts mostly leading the price changes. Backtesting analysis shows that a straightforward strategy based on the cross-correlation where one buys/sells a coin if the daily number of posts about it is greater/less than the previous would have led to a 3x return on investment. Finally, we shed light on the emotional dynamics of the cryptocurrency communities, finding that joy becomes a prominent indicator during upward market performance, while a decline in the market manifests an increase in anger.

cs.CR↗

arXiv2023

Roll in the Tanks! Measuring Left-wing Extremism on Reddit at Scale

Social media's role in the spread and evolution of extremism is a focus of intense study. Online extremists have been involved in the dissemination of online hate, mis- and disinformation, and real-world violence. While the majority of research has focused on right-wing extremism, recent real-world incidents have highlighted the potential for far-left extremists to engage in violence and cause real-world harm as well. In this paper, we present the first large-scale measurement of left-wing extremism on social media. Analyzing 1.3 million posts from 53,000 authors from tankie subreddits, we focus on ``tankies,'' a left-wing community that first arose in the 1950s in support of hardline actions of the USSR and has evolved to support what they call ``Actually Existing Socialist'' countries, e.g., CCP-run China, the USSR, and North Korea. Among other things, our analysis reveals that these groups occupy the periphery of the broader far-left community on Reddit, and their discourse distinctively focus on state-level politics and support for authoritarian regimes, rather than on social justice issues. Finally, we show that tankies have high toxicity scores and use pejorative language, mirroring toxicity patterns reported for other online extremist communities. Our findings provide empirical evidence of the distinct positioning and discourse of left-wing extremist groups on social media.

cs.SI↗