Using RL to Identify Divisive Perspectives Improves LLM s Abilities to Identify Communities on Social Media (2024)

Abstract: The large scale usage of social media, combined with its significant impact, has made it increasingly important to understand it. In particular, identifying user communities, can be helpful for many downstream tasks. However, particularly when models are trained on past data and tested on future, doing this is difficult.In this paper, we hypothesize to take advantage of Large Language Models (LLMs), to better identify user communities. Due to the fact that many LLMs, such as ChatGPT, are fixed and must be treated as black-boxes, we propose an approach to better prompt them, by training a smaller LLM to do this. We devise strategies to train this smaller model, showing how it can improve the larger LLMs ability to detect communities. Experimental results show improvements on Reddit and Twitter data, and the tasks of community detection, bot detection, and news media profiling.

## introduction the rise of social media platforms over the last decade has had a tremendous impact on people's lives, affecting their perspectives on key events such as political elections @xcite and led to the creation of segregated information communities, also known as "echo chambers" @xcite @xcite . following the the principal of social homophily @xcite , these tightly-knit communities consist of like-minded users, which have similar viewpoints and content preferences. identifying these information communities can lead to better performance in a number of important social media related downstream tasks, such as news media profiling (fake news and political bias detection), user content recommendation, trend prediction, crisis monitoring, sentiment analysis, and which users have similar perspectives and thus should be the same community? user 1: user 1 text description user 2: user 2 text description … user 6: user 3 text description output: user 1, user 3. ## related work over the last few years, there has been a number of works analyzing social media, whether it is news media profiling @xcite , fake news detection @xcite , reddit analysis @xcite , bot detection @xcite , or topic analysis @xcite . these works utilize a variety of ml frameworks, such as llms @xcite and graphs @xcite , and evaluate a variety of settings such as crossdomain @xcite and low-resource @xcite ones. a more realistic and more challenging setting to analyze, which we also do, is one in which test samples mention different topics and feature different users than seen at training time. due to their importance, these settings have also recently received more attention @xcite ). an important part of social media analysis is analyzing the users on social media. specifically, prior work @xcite shows how grouping the users into information communities can provide insight for downstream tasks, such as fake news detection @xcite , content recommendation @xcite , or even general analysis @xcite such as how users view major events @xcite . in general, understanding user perspectives and forming these communities, is important, see: app. a. large language models (llms) have been applied to a large amount of social media related tasks, like fake news detection @xcite , as they can capture a large amount of knowledge learned from their extensive pre-training. while they can succeed at many nlp tasks like summarization @xcite , they still struggle on reasoning tasks like needed for social media analysis. however, as we later show, when appropriately prompted, their performance on these tasks improves. prompting llms has been studied in a variety of ways, whether it be chain-of-thought reasoning @xcite , chain-of-hindsight @xcite , self-refinement @xcite or rlhf @xcite . similar to akyurek et al., we aim to train a smaller language model to prompt bigger, frozen llms. similarly, improving llms using feedback has also received increasing research attention, across a variety of tasks, such as summarization @xcite @xcite . however, compared to tasks like summarization and action planning, social media analysis requires a more nuanced analysis, which affects the way we train our models (i.e. reward functions), and the feedback we provide. ## model our goal in this paper is to improve big, frozen llms performance on social media related tasks, specifically detecting user communities, as described in sec. 3.1. to do this, we train a smaller llm to add additional text, which we call a "focus area" (sec 3.2), to the prompt of the bigger one. we train the smaller model first using supervised learning (sec. 3.3), and then reinforcement learning (sec. 3.4). similar to akyurek et al., we refer to the bigger, frozen llm as llm task , and the smaller one as llm prompt . ## discussion we analyze our best rl llm prompt model, with chatgpt. we do an ablation study of our reward functions, ( 5.1), then a human analysis of generated focus areas, (5.2), then case studies (app. h), then an analysis of llm detected user communities for factuality detection (app. i), and finally discuss the real world impact of our approach ( 5.3). ## conclusion in this paper, we proposed to use large, frozen llms to detect user information communities on social media, particularly in the challenging settings of emerging news events, where test data features topics and time periods not seen at training time. we then improved this llms performance, by training a smaller lm (llm prompt ) to generate a focus area, an additional sentence to feed into the bigger llm. this focus area focuses the llm on the relevant aspects of users that would result in better information communities, such as divisive issues. experimental results on reddit and twitter data showed performance improvements in detecting communities when using focus areas, even on emerging news events. we evaluated four different datasets, all from time periods occurring after the training set, and three different domains, making the task very challenging. further, we learned meaningful communities, that lead to improvements on the downstream task of source profiling (factuality/bias detection). our future work is primarily to generate better focus areas. we are particularly interested in exploring different reward functions, such as using different off the shelf models, to improve the quality of focus areas and make them more useful. we also want to explore the impact of multiple stages of sft and rl training, where focus areas can improve across stages. finally, we aim to conduct our experiments at a larger scale, on bigger datasets, so we can further study the real world impact of our framework.