arXiv subjects

Keith Burghardt

Publications and source records attributed to Keith Burghardt.

At least 19 recordsLinked to original sources

arXiv2026

NOMADD: Numerical Optimization of Models Adapting to Data Drift

Tabular model performance degrades when feature distributions change over time or the relationship between features and outcome variables change over time, known as data drift and concept drift, respectively. These issues are challenging to mitigate in real time because labeled data may not be immediately available, or re-training a model could be impractical. While tools exist to reduce drift, they are typically bespoke to neural network architectures and adapt how models are trained. In this paper, we offer an alternative post-hoc method to reduce concept drift, which is applicable to a variety of models, from trees to neural networks to tabular foundation models. This new tool is especially useful when constraints, such as high model accuracy, bounded inference time, or model size requires users to choose between different models for their specific use-cases. Our algorithm fits the base model separately on each labeled training period, measures how its parameters evolve against a single anchor model pooled over all of those periods, compresses those changes with a low-rank factorization, and extrapolates each latent factor forward with a damped, regularized forecast. On the 18-dataset Drift-Resilient TabPFN benchmark, evaluated under that benchmark's own protocol and metric, the extrapolation improves every base family it is applied to, and achieves performance competitive with the state-of-the-art Drift-Resilient TabPFN with seconds of training. In contrast, Drift-Resilient TabPFN requires pre-training on millions of synthetic datasets over approximately 1,300 GPU-hours, and is orders of magnitude slower in inference (depending on the model). In the discussion, we explore the promise and challenges of extending this tool to other modalities.

cs.LG↗

arXiv2026

FAMOSE: A ReAct Approach to Automated Feature Discovery

Feature engineering remains a critical yet challenging bottleneck in machine learning, particularly for tabular data, as identifying optimal features from an exponentially large feature space traditionally demands substantial domain expertise. To address this challenge, we introduce FAMOSE (Feature AugMentation and Optimal Selection agEnt), a novel framework that leverages the ReAct paradigm to autonomously explore, generate, and refine features while integrating feature selection and evaluation tools within an agent architecture. To our knowledge, FAMOSE represents the first application of an agentic ReAct framework to automated feature engineering, especially for both regression and classification tasks. Extensive experiments demonstrate that FAMOSE is at or near the state-of-the-art on classification tasks (especially tasks with more than 10K instances, where ROC-AUC increases 0.23% on average), and achieves the state-of-the-art for regression tasks by reducing RMSE by 2.0% on average, while remaining more robust to errors than other algorithms. We hypothesize that FAMOSE's strong performance is because ReAct allows the LLM context window to record (via iterative feature discovery and evaluation steps) what features did or did not work. This is similar to a few-shot prompt and guides the LLM to invent better, more innovative features. Our work offers evidence that AI agents are remarkably effective in solving problems that require highly inventive solutions, such as feature engineering.

cs.LG↗

arXiv2025

SoMeR: Multi-View User Representation Learning for Social Media

Social media user representation learning aims to capture user preferences, interests, and behaviors in low-dimensional vector representations. These representations are critical to a range of social problems, including predicting user behaviors and detecting inauthentic accounts. However, existing methods are either designed for commercial applications, or rely on specific features like text contents, activity patterns, or platform metadata, failing to holistically model user behavior across different modalities. To address these limitations, we propose SoMeR, a Social Media user Representation learning framework that incorporates temporal activities, text contents, profile information, and network interactions to learn comprehensive user portraits. SoMeR encodes user post streams as sequences of time-stamped textual features, uses transformers to embed this along with profile data, and jointly trains with link prediction and contrastive learning objectives to capture user similarity. We demonstrate SoMeR's versatility through three applications: 1) Identifying information operation driver accounts, 2) Measuring online polarization after major events, and 3) Predicting future user participation in Reddit hate communities. SoMeR provides new solutions to better understand user behavior in the socio-political domains, enabling more informed decisions and interventions.

cs.SI↗

arXiv2024

Universal Patterns in the Long-term Growth of Urban Infrastructure in U.S. Cities from 1900 to 2015

Despite the rapid growth of cities in the past century, our quantitative, in-depth understanding of how cities grow remains limited due to a consistent lack of historical data. Thus, the scaling laws between a city's features and its population as they evolve over time, known as temporal city scaling, is under-explored, especially for time periods spanning multiple decades. In this paper, we leverage novel data sources such as the Historical Settlement Data Compilation for the U.S. (HISDAC-US), and analyze the temporal scaling laws of developed area, building indoor area, building footprint area, and road length and other road network statistics for nearly all metropolitan areas in the U.S. from 1900 to 2015. We find that scaling exponents vary dramatically between cities as a function of their size and location. Three notable patterns emerge. First, scaling law exponents imply many, but not all, metropolitan areas are becoming less dense and indoor area per capita increases as cities grow, in contrast to expectations. Second, larger cities tend to have a smaller scaling exponent than smaller cities. Third, scaling exponents (and growth patterns) are similar between nearby cities. These results show a long-term trend that could harm urban sustainability as previously dense populations are rapidly spreading out into undeveloped land. Moreover, the regional similarity of long-term urban growth patterns implies that city evolution and sustainability patterns are more interconnected than prior research has suggested. These results help urban planners and scientists understand universal, long-term patterns of city growth across the US.

physics.soc-ph↗

arXiv2024

The Peripatetic Hater: Predicting Movement Among Hate Subreddits

Many online hate groups exist to disparage others based on race, gender identity, sex, or other characteristics. The accessibility of these communities allows users to join multiple types of hate groups (e.g., a racist community and a misogynistic community), raising the question of whether users who join additional types of hate communities could be further radicalized compared to users who stay in one type of hate group. However, little is known about the dynamics of joining multiple types of hate groups, nor the effect of these groups on peripatetic users. We develop a new method to classify hate subreddits and the identities they disparage, then apply it to understand better how users come to join different types of hate subreddits. The hate classification technique utilizes human-validated deep learning models to extract the protected identities attacked, if any, across 168 subreddits. We find distinct clusters of subreddits targeting various identities, such as racist subreddits, xenophobic subreddits, and transphobic subreddits. We show that when users become active in their first hate subreddit, they have a high likelihood of becoming active in additional hate subreddits of a different category. We also find that users who join additional hate subreddits, especially those of a different category develop a wider hate group lexicon. These results then lead us to train a deep learning model that, as we demonstrate, usefully predicts the hate categories in which users will become active based on post text replied to and written. The accuracy of this model may be partly driven by peripatetic users often using the language of hate subreddits they eventually join. Overall, these results highlight the unique risks associated with hate communities on a social media platform, as discussion of alternative targets of hate may lead users to target more protected identities.

cs.SI↗

arXiv2024

Data-Driven Estimation of Heterogeneous Treatment Effects

Estimating how a treatment affects different individuals, known as heterogeneous treatment effect estimation, is an important problem in empirical sciences. In the last few years, there has been a considerable interest in adapting machine learning algorithms to the problem of estimating heterogeneous effects from observational and experimental data. However, these algorithms often make strong assumptions about the observed features in the data and ignore the structure of the underlying causal model, which can lead to biased estimation. At the same time, the underlying causal mechanism is rarely known in real-world datasets, making it hard to take it into consideration. In this work, we provide a survey of state-of-the-art data-driven methods for heterogeneous treatment effect estimation using machine learning, broadly categorizing them as methods that focus on counterfactual prediction and methods that directly estimate the causal effect. We also provide an overview of a third category of methods which rely on structural causal models and learn the model structure from data. Our empirical evaluation under various underlying structural model mechanisms shows the advantages and deficiencies of existing estimators and of the metrics for measuring their performance.

cs.LG↗

arXiv2024

Bias Reduction in Social Networks through Agent-Based Simulations

Online social networks use recommender systems to suggest relevant information to their users in the form of personalized timelines. Studying how these systems expose people to information at scale is difficult to do as one cannot assume each user is subject to the same timeline condition and building appropriate evaluation infrastructure is costly. We show that a simple agent-based model where users have fixed preferences affords us the ability to compare different recommender systems (and thus different personalized timelines) in their ability to skew users' perception of their network. Importantly, we show that a simple greedy algorithm that constructs a feed based on network properties reduces such perception biases comparable to a random feed. This underscores the influence network structure has in determining the effectiveness of recommender systems in the social network context and offers a tool for mitigating perception biases through algorithmic feed construction.

cs.SI↗

arXiv2024

Posts of Peril: Detecting Information About Hazards in Text

Socio-linguistic indicators of affectively-relevant phenomena, such as emotion or sentiment, are often extracted from text to better understand features of human-computer interactions, including on social media. However, an indicator that is often overlooked is the presence or absence of information concerning harms or hazards. Detecting such indicators in text is important because substantial research demonstrates that negative events are more likely to be attended to, and more likely to elicit a response. In addition, statements about hazards are often found to be more believable than statements about benefits. Here, we develop a new model to detect information concerning hazards, trained on a new collection of annotated X posts. We show that not only does this model perform well, but that the hazard information it extracts is not strongly correlated with such widely used indicators as moral outrage, sentiment, and emotions. To demonstrate the utility of our tool, we apply it to two datasets of X posts that discuss important geopolitical events, namely the 2023 Israel-Hamas war and the 2022 French national election. In both cases, we find that hazard information, especially information concerning conflict, is common. We extract accounts associated with information campaigns from each data set to explore how information about hazards could be used to attempt to influence geopolitical events. We find that inorganic accounts representing the viewpoints of weaker sides in a conflict often discuss hazards to civilians, potentially as a way to elicit aid for the weaker side. Moreover, the rate at which these hazards are mentioned differs markedly from organic accounts, likely reflecting information operators' efforts to frame the given geopolitical event for strategic purposes. These results are first steps towards exploring hazards within an information warfare environment.

cs.LG↗

arXiv2024

Assessing How Hate, Counterspeech, and Toxicity Affect Hate Group Newcomers

Counterspeech has gained attention as a strategy to reduce hate speech on social media. Although previous studies suggest that counterspeech can reduce hate speech, little is known about its effects on participation in online hate communities. Relatedly, we lack an understanding about the degree of hostility in counterspeech. Hostile counterspeech may increase online conflict, potentially hardening the positions of hate adherents, and further eroding online environments. Here, we analyzed the effect of counterspeech on 16,513 newcomers across 104 hate subreddits (forums within Reddit.com). We devised an LLM-based counterspeech detection approach that outperforms specialized models trained on existing datasets, then examined the presence, and effects of, hostility. While counterspeech comments are less toxic than hate speech comments, they are almost twice as toxic as other discourse within hate subreddits. We then evaluated the effect of counterspeech on newcomer engagement in hate subreddits. We found that newcomers using hate speech who receive counterspeech are less likely to continue posting within these hate subreddits, rather than becoming galvanized. We speculate that, instead of constituting ardent hate adherents, readily-dissuaded newcomers may merely be toying with beliefs that are proscribed in other contexts. Although we found no association between the toxicity of counterspeech and its effects on user retention, consistent with prior research regarding the harmful effects of toxic speech, we found that toxic counterspeech increases the probability of continued hostility from hate users within the same discussion.

cs.CY↗

arXiv2024

Large Language Models Reveal Information Operation Goals, Tactics, and Narrative Frames

Adversarial information operations can destabilize societies by undermining fair elections, manipulating public opinions on policies, and promoting scams. Despite their widespread occurrence and potential impacts, our understanding of influence campaigns is limited by manual analysis of messages and subjective interpretation of their observable behavior. In this paper, we explore whether these limitations can be mitigated with large language models (LLMs), using GPT-3.5 as a case-study for coordinated campaign annotation. We first use GPT-3.5 to scrutinize 126 identified information operations spanning over a decade. We utilize a number of metrics to quantify the close (if imperfect) agreement between LLM and ground truth descriptions. We next extract coordinated campaigns from two large multilingual datasets from X (formerly Twitter) that respectively discuss the 2022 French election and 2023 Balikaran Philippine-U.S. military exercise in 2023. For each coordinated campaign, we use GPT-3.5 to analyze posts related to a specific concern and extract goals, tactics, and narrative frames, both before and after critical events (such as the date of an election). While the GPT-3.5 sometimes disagrees with subjective interpretation, its ability to summarize and interpret demonstrates LLMs' potential to extract higher-order indicators from text to provide a more complete picture of the information campaigns compared to previous methods.

cs.CL↗

arXiv2024

IsamasRed: A Public Dataset Tracking Reddit Discussions on Israel-Hamas Conflict

The conflict between Israel and Palestinians significantly escalated after the October 7, 2023 Hamas attack, capturing global attention. To understand the public discourse on this conflict, we present a meticulously compiled dataset-IsamasRed-comprising nearly 400,000 conversations and over 8 million comments from Reddit, spanning from August 2023 to November 2023. We introduce an innovative keyword extraction framework leveraging a large language model to effectively identify pertinent keywords, ensuring a comprehensive data collection. Our initial analysis on the dataset, examining topics, controversy, emotional and moral language trends over time, highlights the emotionally charged and complex nature of the discourse. This dataset aims to enrich the understanding of online discussions, shedding light on the complex interplay between ideology, sentiment, and community engagement in digital spaces.

cs.SI↗

arXiv2024

GET-Tok: A GenAI-Enriched Multimodal TikTok Dataset Documenting the 2022 Attempted Coup in Peru

TikTok is one of the largest and fastest-growing social media sites in the world. TikTok features, however, such as voice transcripts, are often missing and other important features, such as OCR or video descriptions, do not exist. We introduce the Generative AI Enriched TikTok (GET-Tok) data, a pipeline for collecting TikTok videos and enriched data by augmenting the TikTok Research API with generative AI models. As a case study, we collect videos about the attempted coup in Peru initiated by its former President, Pedro Castillo, and its accompanying protests. The data includes information on 43,697 videos published from November 20, 2022 to March 1, 2023 (102 days). Generative AI augments the collected data via transcripts of TikTok videos, text descriptions of what is shown in the videos, what text is displayed within the video, and the stances expressed in the video. Overall, this pipeline will contribute to a better understanding of online discussion in a multimodal setting with applications of Generative AI, especially outlining the utility of this pipeline in non-English-language social media. Our code used to produce the pipeline is in a public Github repository: https://github.com/gabbypinto/GET-Tok-Peru.

cs.SI↗

arXiv2024

Auditing Elon Musk's Impact on Hate Speech and Bots

On October 27th, 2022, Elon Musk purchased Twitter, becoming its new CEO and firing many top executives in the process. Musk listed fewer restrictions on content moderation and removal of spam bots among his goals for the platform. Given findings of prior research on moderation and hate speech in online communities, the promise of less strict content moderation poses the concern that hate will rise on Twitter. We examine the levels of hate speech and prevalence of bots before and after Musk's acquisition of the platform. We find that hate speech rose dramatically upon Musk purchasing Twitter and the prevalence of most types of bots increased, while the prevalence of astroturf bots decreased.

cs.SI↗

arXiv2023

Unmasking the Web of Deceit: Uncovering Coordinated Activity to Expose Information Operations on Twitter

Social media platforms, particularly Twitter, have become pivotal arenas for influence campaigns, often orchestrated by state-sponsored information operations (IOs). This paper delves into the detection of key players driving IOs by employing similarity graphs constructed from behavioral pattern data. We unveil that well-known, yet underutilized network properties can help accurately identify coordinated IO drivers. Drawing from a comprehensive dataset of 49 million tweets from six countries, which includes multiple verified IOs, our study reveals that traditional network filtering techniques do not consistently pinpoint IO drivers across campaigns. We first propose a framework based on node pruning that emerges superior, particularly when combining multiple behavioral indicators across different networks. Then, we introduce a supervised machine learning model that harnesses a vector representation of the fused similarity network. This model, which boasts a precision exceeding 0.95, adeptly classifies IO drivers on a global scale and reliably forecasts their temporal engagements. Our findings are crucial in the fight against deceptive influence campaigns on social media, helping us better understand and detect them.

cs.SI↗

arXiv2023

Socio-Linguistic Characteristics of Coordinated Inauthentic Accounts

Online manipulation is a pressing concern for democracies, but the actions and strategies of coordinated inauthentic accounts, which have been used to interfere in elections, are not well understood. We analyze a five million-tweet multilingual dataset related to the 2017 French presidential election, when a major information campaign led by Russia called "#MacronLeaks" took place. We utilize heuristics to identify coordinated inauthentic accounts and detect attitudes, concerns and emotions within their tweets, collectively known as socio-linguistic characteristics. We find that coordinated accounts retweet other coordinated accounts far more than expected by chance, while being exceptionally active just before the second round of voting. Concurrently, socio-linguistic characteristics reveal that coordinated accounts share tweets promoting a candidate at three times the rate of non-coordinated accounts. Coordinated account tactics also varied in time to reflect news events and rounds of voting. Our analysis highlights the utility of socio-linguistic characteristics to inform researchers about tactics of coordinated accounts and how these may feed into online social manipulation.

cs.SI↗

arXiv2023

Clique Densification in Networks

Real-world networks are rarely static. Recently, there has been increasing interest in both network growth and network densification, in which the number of edges scales superlinearly with the number of nodes. Less studied but equally important, however, are scaling laws of higher-order cliques, which can drive clustering and network redundancy. In this paper, we study how cliques grow with network size, by analyzing several empirical networks from emails to Wikipedia interactions. Our results show superlinear scaling laws whose exponents increase with clique size, in contrast to predictions from a previous model. We then show that these results are in qualitative agreement with a new model that we propose, the Local Preferential Attachment Model, where an incoming node links not only to a target node but also to its higher-degree neighbors. Our results provide new insights into how networks grow and where network redundancy occurs.

cs.SI↗

arXiv2023

No Love Among Haters: Negative Interactions Reduce Hate Community Engagement

While online hate groups pose significant risks to the health of online platforms and safety of marginalized groups, little is known about what causes users to become active in hate groups and the effect of social interactions on furthering their engagement. We address this gap by first developing tools to find hate communities within Reddit, and then augment 11 subreddits extracted with 14 known hateful subreddits (25 in total). Using causal inference methods, we evaluate the effect of replies on engagement in hateful subreddits by comparing users who receive replies to their first comment (the treatment) to equivalent control users who do not. We find users who receive replies are less likely to become engaged in hateful subreddits than users who do not, while the opposite effect is observed for a matched sample of similar-sized non-hateful subreddits. Using the Google Perspective API and VADER, we discover that hateful community first-repliers are more toxic, negative, and attack the posters more often than non-hateful first-repliers. In addition, we uncover a negative correlation between engagement and attacks or toxicity of first-repliers. We simulate the cumulative engagement of hateful and non-hateful subreddits under the contra-positive scenario of friendly first-replies, finding that attacks dramatically reduce engagement in hateful subreddits. These results counter-intuitively imply that, although under-moderated communities allow hate to fester, the resulting environment is such that direct social interaction does not encourage further participation, thus endogenously constraining the harmful role that these communities could play as recruitment venues for antisocial beliefs.

cs.SI↗

arXiv2023

Leveraging Label Correlations in a Multi-label Setting: A Case Study in Emotion

Detecting emotions expressed in text has become critical to a range of fields. In this work, we investigate ways to exploit label correlations in multi-label emotion recognition models to improve emotion detection. First, we develop two modeling approaches to the problem in order to capture word associations of the emotion words themselves, by either including the emotions in the input, or by leveraging Masked Language Modeling (MLM). Second, we integrate pairwise constraints of emotion representations as regularization terms alongside the classification loss of the models. We split these terms into two categories, local and global. The former dynamically change based on the gold labels, while the latter remain static during training. We demonstrate state-of-the-art performance across Spanish, English, and Arabic in SemEval 2018 Task 1 E-c using monolingual BERT-based models. On top of better performance, we also demonstrate improved robustness. Code is available at https://github.com/gchochla/Demux-MEmo.

cs.CL↗