Social scientists use surveys to probe the opinions and beliefs of populations, but these methods are slow, costly, and prone to biases. Recent advances in large language models (LLMs) enable creating computational representations or "digital twins" of populations that generate human-like responses mimicking the population's language, styles, and attitudes. We introduce Community-Cross-Instruct, an unsupervised framework for aligning LLMs to online communities to elicit their beliefs. Given a corpus of a community's online discussions, Community-Cross-Instruct automatically generates instruction-output pairs by an advanced LLM to (1) finetune an foundational LLM to faithfully represent that community, and (2) evaluate the alignment of the finetuned model to the community. We demonstrate the method's utility in accurately representing political and fitness communities on Reddit. Unlike prior methods requiring human-authored instructions, Community-Cross-Instruct generates instructions in a fully unsupervised manner, enhancing scalability and generalization across domains. This work enables cost-effective and automated surveying of diverse online communities.

Language models (LMs) are known to represent the perspectives of some social groups better than others, which may impact their performance, especially on subjective tasks such as content moderation and hate speech detection. To explore how LMs represent different perspectives, existing research focused on positional alignment, i.e., how closely the models mimic the opinions and stances of different groups, e.g., liberals or conservatives. However, human communication also encompasses emotional and moral dimensions. We define the problem of affective alignment, which measures how LMs' emotional and moral tone represents those of different groups. By comparing the affect of responses generated by 36 LMs to the affect of Twitter messages, we observe significant misalignment of LMs with both ideological groups. This misalignment is larger than the partisan divide in the U.S. Even after steering the LMs towards specific ideological perspectives, the misalignment and liberal tendencies of the model persist, suggesting a systemic bias within LMs.

Effective communication during health crises is critical, with social media serving as a key platform for public health experts (PHEs) to engage with the public. However, it also amplifies pseudo-experts promoting contrarian views. Despite its importance, the role of emotional and moral language in PHEs' communication during COVID-19 remains under explored. This study examines how PHEs and pseudo-experts communicated on Twitter during the pandemic, focusing on emotional and moral language and their engagement with political elites. Analyzing tweets from 489 PHEs and 356 pseudo-experts from January 2020 to January 2021, alongside public responses, we identified key priorities and differences in messaging strategy. PHEs prioritize masking, healthcare, education, and vaccines, using positive emotional language like optimism. In contrast, pseudo-experts discuss therapeutics and lockdowns more frequently, employing negative emotions like pessimism and disgust. Negative emotional and moral language tends to drive engagement, but positive language from PHEs fosters positivity in public responses. PHEs exhibit liberal partisanship, expressing more positivity towards liberals and negativity towards conservative elites, while pseudo-experts show conservative partisanship. These findings shed light on the polarization of COVID-19 discourse and underscore the importance of strategic use of emotional and moral language by experts to mitigate polarization and enhance public trust.

Socio-linguistic indicators of text, such as emotion or sentiment, are often extracted using neural networks in order to better understand features of social media. One indicator that is often overlooked, however, is the presence of hazards within text. Recent psychological research suggests that statements about hazards are more believable than statements about benefits (a property known as negatively biased credulity), and that political liberals and conservatives differ in how often they share hazards. Here, we develop a new model to detect information concerning hazards, trained on a new collection of annotated X posts, as well as urban legends annotated in previous work. We show that not only does this model perform well (outperforming, e.g., zero-shot human annotator proxies, such as GPT-4) but that the hazard information it extracts is not strongly correlated with other indicators, namely moral outrage, sentiment, emotions, and threat words. (That said, consonant with expectations, hazard information does correlate positively with such emotions as fear, and negatively with emotions like joy.) We then apply this model to three datasets: X posts about COVID-19, X posts about the 2023 Hamas-Israel war, and a new expanded collection of urban legends. From these data, we uncover words associated with hazards unique to each dataset as well as differences in this language between groups of users, such as conservatives and liberals, which informs what these groups perceive as hazards. We further show that information about hazards peaks in frequency after major hazard events, and therefore acts as an automated indicator of such events. Finally, we find that information about hazards is especially prevalent in urban legends, which is consistent with previous work that finds that reports of hazards are more likely to be both believed and transmitted.

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.

User representation learning aims to capture user preferences, interests, and behaviors in low-dimensional vector representations. These representations have widespread applications in recommendation systems and advertising; however, existing methods typically rely on specific features like text content, activity patterns, or platform metadata, failing to holistically model user behavior across different modalities. To address this limitation, we propose SoMeR, a Social Media user Representation learning framework that incorporates temporal activities, text content, profile information, and network interactions to learn comprehensive user portraits. SoMeR encodes user post streams as sequences of timestamped 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 two applications: 1) Identifying inauthentic accounts involved in coordinated influence operations by detecting users posting similar content simultaneously, and 2) Measuring increased polarization in online discussions after major events by quantifying how users with different beliefs moved farther apart in the embedding space. SoMeR's ability to holistically model users enables new solutions to important problems around disinformation, societal tensions, and online behavior understanding.

In-context Learning (ICL) has emerged as a powerful paradigm for performing natural language tasks with Large Language Models (LLM) without updating the models' parameters, in contrast to the traditional gradient-based finetuning. The promise of ICL is that the LLM can adapt to perform the present task at a competitive or state-of-the-art level at a fraction of the cost. The ability of LLMs to perform tasks in this few-shot manner relies on their background knowledge of the task (or task priors). However, recent work has found that, unlike traditional learning, LLMs are unable to fully integrate information from demonstrations that contrast task priors. This can lead to performance saturation at suboptimal levels, especially for subjective tasks such as emotion recognition, where the mapping from text to emotions can differ widely due to variability in human annotations. In this work, we design experiments and propose measurements to explicitly quantify the consistency of proxies of LLM priors and their pull on the posteriors. We show that LLMs have strong yet inconsistent priors in emotion recognition that ossify their predictions. We also find that the larger the model, the stronger these effects become. Our results suggest that caution is needed when using ICL with larger LLMs for affect-centered tasks outside their pre-training domain and when interpreting ICL results.

Researchers have raised awareness about the harms of aggregating labels especially in subjective tasks that naturally contain disagreements among human annotators. In this work we show that models that are only provided aggregated labels show low confidence on high-disagreement data instances. While previous studies consider such instances as mislabeled, we argue that the reason the high-disagreement text instances have been hard-to-learn is that the conventional aggregated models underperform in extracting useful signals from subjective tasks. Inspired by recent studies demonstrating the effectiveness of learning from raw annotations, we investigate classifying using Multiple Ground Truth (Multi-GT) approaches. Our experiments show an improvement of confidence for the high-disagreement instances.

Recent advances in NLP have improved our ability to understand the nuanced worldviews of online communities. Existing research focused on probing ideological stances treats liberals and conservatives as separate groups. However, this fails to account for the nuanced views of the organically formed online communities and the connections between them. In this paper, we study discussions of the 2020 U.S. election on Twitter to identify complex interacting communities. Capitalizing on this interconnectedness, we introduce a novel approach that harnesses message passing when finetuning language models (LMs) to probe the nuanced ideologies of these communities. By comparing the responses generated by LMs and real-world survey results, our method shows higher alignment than existing baselines, highlighting the potential of using LMs in revealing complex ideologies within and across interconnected mixed-ideology communities.

The rise in eating disorders, a dangerous mental health condition with high mortality and morbidity, has been linked to the proliferation of idealized body images on social media. However, the link between social media and eating disorders is far more complex. We argue that social media platforms create a feedback loop that amplifies the growth of content and communities that promote eating disorders like anorexia and bulimia. Specifically, social media platforms make it easy for vulnerable individuals to find and connect to like-minded others, while group dynamic processes encourage them to stay engaged within communities that promote and glorify harmful behaviors linked to eating disorders. We characterize this dynamic empirically through a combination of network and language analysis. We describe a novel framework that leverages large language models to analyze the discourse within online communities and probe their attitudes on topics related to eating disorders to identify potentially harmful content. Our work emphasizes the need for better social media moderation to disrupt harmful feedback loops and protect vulnerable individuals.