Stance classification, the task of predicting the viewpoint of an author on a subject of interest, has long been a focal point of research in domains ranging from social science to machine learning. Current stance detection methods rely predominantly on manual annotation of sentences, followed by training a supervised machine learning model. However, this manual annotation process requires laborious annotation effort, and thus hampers its potential to generalize across different contexts. In this work, we investigate the use of Large Language Models (LLMs) as a stance detection methodology that can reduce or even eliminate the need for manual annotations. We investigate 10 open source models and 7 prompting schemes, finding that LLMs are competitive with in-domain supervised models but are not necessarily consistent in their performance. We also fine-tuned the LLMs, but discovered that fine-tuning process does not necessarily lead to better performance. In general, we discover that LLMs do not routinely outperform their smaller supervised machine learning models, and thus call for stance detection to be a benchmark for which LLMs also optimize for. The code used in this study is available at https://github.com/ijcruic/LLM-Stance-Labeling .

Large Language Models (LLMs) have demonstrated remarkable capabilities in executing tasks based on natural language queries. However, these models, trained on curated datasets, inherently embody biases ranging from racial to national and gender biases. It remains uncertain whether these biases impact the performance of LLMs for certain tasks. In this study, we investigate the political biases of LLMs within the stance classification task, specifically examining whether these models exhibit a tendency to more accurately classify politically-charged stances. Utilizing three datasets, seven LLMs, and four distinct prompting schemes, we analyze the performance of LLMs on politically oriented statements and targets. Our findings reveal a statistically significant difference in the performance of LLMs across various politically oriented stance classification tasks. Furthermore, we observe that this difference primarily manifests at the dataset level, with models and prompting schemes showing statistically similar performances across different stance classification datasets. Lastly, we observe that when there is greater ambiguity in the target the statement is directed towards, LLMs have poorer stance classification accuracy.

Public opinion of military organizations plays a crucial role in their ability to recruit talented individuals. As recruitment increasingly extends into digital spaces like social media, assessing the stance of social media users toward online military content becomes essential. However, there is a notable lack of data for analyzing opinions on military recruitment efforts online, compounded by challenges in stance labeling, which is key to understanding public perceptions. Despite the importance of stance analysis for effective online military recruitment, generating human-annotated, in-domain stance labels is resource-intensive. In this paper, we address both the challenges of stance labeling and the scarcity of data on public opinions of online military recruitment by introducing and releasing the DIVERSE dataset1. This dataset comprises comments from videos on the U.S. Army’s official YouTube channel. We employed a state-of-the-art weak supervision approach, leveraging large language models to label the stance of each comment toward its respective video and the U.S. Army. Our findings indicate that the U.S. Army’s videos began attracting a substantial number of comments post-2021, with a generally balanced stance distribution among supportive, oppositional, and neutral comments, though with a slight skew towards oppositional over supportive comments.

Despite rapid development, current bot detection models still face challenges in dealing with incomplete data and cross-platform applications. In this paper, we propose BotBuster, a social bot detector built with the concept of a mixture of experts approach. Each expert is trained to analyze a portion of account information, e.g. username, and are combined to estimate the probability that the account is a bot. Experiments on 10 Twitter datasets show that BotBuster outperforms popular bot-detection baselines (avg F1=73.54 vs avg F1=45.12). This is accompanied with F1=60.04 on a Reddit dataset and F1=60.92 on an external evaluation set. Further analysis shows that only 36 posts is required for a stable bot classification. Investigation shows that bot post features have changed across the years and can be difficult to differentiate from human features, making bot detection a difficult and ongoing problem.

This case study investigates a recent Russian disinformation narrative about U.S. biolabs and the development of biological weapons in Ukraine. This disinformation campaign was officially initiated by the Russian government, including the Russian Ministry of Defense, and was disseminated by official state-funded Russian media. In their announcement, Russian officials condemned pharmaceutical companies such as Pfizer and Moderna, as well as the U.S. military, for developing biological weapons in Ukraine. Four datasets with tweets were collected in English and Russian to analyze narratives and communities involved in the Ukrainian biolabs conversations on Twitter.This study aims to investigate how this disinformation theme was propagated in two languages on Twitter. Twitter social media data analysis, network influence metrics, coordination analysis, Leiden clustering, and qualitative data analysis were used in this study. The results have demonstrated that this particular disinformation campaign targeted both English and Russian-speaking audiences with coordinated inauthentic amplification to spread this disinformation to support Russia’s invasion of Ukraine. However, we found that this narrative was also naturally propagated by anti-vax and conservative communities among English-speaking users on Twitter.

Stance detection identifies a person’s evaluation of a subject, and is a crucial component for many downstream applications. In application, stance detection requires training a machine learning model on an annotated dataset and applying the model on another to predict stances of text snippets. This cross-dataset model generalization poses three central questions, which we investigate using stance classification models on 7 publicly available English Twitter datasets ranging from 297 to 48,284 instances. (1) Are stance classification models generalizable across datasets? We construct a single dataset model to train/test dataset-against-dataset, finding models do not generalize well (avg F1=0.33). (2) Can we improve the generalizability by aggregating datasets? We find a multi dataset model built on the aggregation of datasets has an improved performance (avg F1=0.69). (3) Given a model built on multiple datasets, how much additional data is required to fine-tune it? We find it challenging to ascertain a minimum number of data points due to the lack of pattern in performance. Investigating possible reasons for the choppy model performance we find that texts are not easily differentiable by stances, nor are annotations consistent within and across datasets. Our observations emphasize the need for an aggregated dataset as well as consistent labels for the generalizability of models.

TikTok is a popular new social media, where users express themselves through short video clips. A common form of interaction on the platform is participating in "challenges", which are songs and dances for users to iterate upon. Challenge contagion can be measured through replication reach, i.e., users uploading videos of their participation in the challenges. The uniqueness of the TikTok platform where both challenge content and user preferences are evolving requires the combination of challenge and user representation. This paper investigates social contagion of TikTok challenges through predicting a user's participation. We propose a novel deep learning model, deepChallenger, to learn and combine latent user and challenge representations from past videos to perform this user-challenge prediction task. We collect a dataset of over 7,000 videos from 12 trending challenges on the ForYouPage, the app's landing page, and over 10,000 videos from 1303 users. Extensive experiments are conducted and the results show that our proposed deepChallenger (F1=0.494) outperforms baselines (F1=0.188) in the prediction task.