Using AI to Predict User Opinions on Social Media

User stance prediction refers to figuring out how individuals feel about certain topics based on their social media posts. This can be especially important in today's world where social media is a crucial platform for people to express their opinions. In particular, understanding how people feel about new or unfolding events can be helpful for organizations and policymakers.

In this study, we look at how Large Language Models (LLMs), which are advanced artificial intelligence systems, can predict a user's stance even when the posts don't directly mention the topic being discussed. This ability relies on interpreting tweets that don't have explicit references to the target subject.

#The Challenge of Traditional Stance Detection

Traditionally, stance detection has focused on analyzing individual posts to see if they are in favor of, against, or neutral toward a particular topic. Most studies have looked at this on a post-by-post basis, but few have tackled the bigger picture: understanding a user's overall stance based on various posts, especially those that do not target a specific issue.

This task is tough because it requires not only understanding language but also having insight into the background and beliefs of users. Existing methods often rely heavily on detailed information about specific topics, which is not always available, especially with new or developing events.

#Role of Large Language Models

Large language models, such as those based on AI, can perform many text-related tasks without needing extensive training on specific topics, which makes them very adaptable. In this research, we explore if these models can help overcome the limits of traditional methods in predicting user stances by using posts that do not mention the specific target directly.

The findings indicate that LLMs can predict user stances reasonably well even when they only have a few target-agnostic posts to work with. This opens up new possibilities for understanding public opinion on emerging topics where direct information is lacking.

#The Importance of the User-Level Stance Dataset

For this study, we used a unique dataset of tweets from 1,000 Twitter users collected over several months. This dataset is important because it contains both tweets that refer to specific topics and tweets that do not. This mix allows us to assess how effectively LLMs can predict stances when information is indirect.

In our dataset, users shared opinions on several trending topics, including political matters and social issues related to the COVID-19 pandemic. The tweets that directly referred to a topic helped us assign a stance to the users. On the other hand, target-agnostic tweets, which did not explicitly mention the topics, still provided useful insights into users' views.

#Methods for Stance Prediction

We implemented two main approaches to use LLMs for stance prediction. The first method involved inputting a collection of tweets from a user to generate a single prediction. The second method analyzed one tweet at a time to generate predictions and then combined the results to form an overall stance for the user.

Both methods were designed not to require prior training on labeled data, which means they can operate in a zero-shot mode. We also tested traditional machine learning methods to compare their performance against that of LLMs.

#Performance Results

Our results showed that the LLMs significantly outperformed traditional models in predicting user stances from target-agnostic posts. The LLMs were particularly effective when they received more tweets, which points toward the value of having multiple perspectives from the same user.

Interestingly, while the LLMs performed well, the performance varied depending on the topic. For example, predictions related to a specific political figure were more accurate than those related to social issues. This variability indicates that the context of the topic can influence the predictive performance.

#Importance of Keywords and User Characteristics

The success of LLMs in stance prediction may be due to two main factors: the presence of significant keywords in target-agnostic tweets and the model's ability to infer deeper user characteristics from their overall behavior on social media.

Some tweets might not mention a target topic directly but still include keywords that indicate a user's opinion. By analyzing these terms, LLMs can make educated guesses about a user's stance.

Furthermore, the cumulative information captured in a user's tweet history may help the model infer the user's core beliefs and values. For instance, a user expressing views related to government regulations might support specific policies if those align with their established beliefs.

#Investigating Model Performance

To better understand how the models were making predictions, we analyzed their performance and how the number of posts impacted the accuracy of predictions. We discovered that supplying multiple tweets at once allowed LLMs to pick up on contextual clues that could improve prediction accuracy. In contrast, analyzing tweets one by one sometimes resulted in misinterpretations, as the broader context may not be clear.

We also fine-tuned the predictions by adjusting thresholds based on training data, which led to improvements in the accuracy of some predictions, especially those related to certain topics. However, even with these adjustments, LLMs that processed tweets as a batch still performed better than those analyzing them individually.

#Final Thoughts on User-Level Stance Prediction

In summary, this research highlights the potential of large language models to predict user stances based on indirect information shared on social media. The ability to make use of target-agnostic tweets opens up new avenues for assessing public opinion, especially in scenarios where direct references are scarce.

While LLMs show great promise, there is still much to learn about the specific mechanisms that lead to accurate predictions. Future work could involve exploring different models, refining data sources, and applying more sophisticated techniques for prompting the LLMs to enhance their performance further.

#Limitations and Future Directions

Our study faced limitations due to the uniqueness of the dataset used, which is the only publicly available resource of its kind. Creating more datasets with a mix of target-specific and target-agnostic tweets would provide a broader basis for understanding user stances.

Looking ahead, researchers could benefit from examining the effectiveness of various LLMs and employing advanced prompting strategies that could enhance the reasoning capabilities of these models. There’s much to explore in the application of LLMs for understanding not just individual stances but the broader trends in public opinion as well.