Analyzing Long COVID Opinions with Language Models

Large Language Models (LLMs) are computer programs that can understand and generate human language. These models have a lot of potential to help us understand opinions shared by people online, especially regarding health topics. This article discusses how LLMs can be used to analyze opinions about Long COVID, a condition where people continue to experience symptoms long after recovering from COVID-19.

The rise of social media has changed how people seek and share information about health. Many individuals turn to platforms like Reddit to discuss their experiences with medical conditions and treatments. As a result, there is a growing need to analyze these discussions to better understand what people think about various health topics.

#What is Opinion Mining?

Opinion mining is the process of determining what people think about a topic by analyzing their written comments and posts. In this context, we focus on the discussions surrounding long COVID. Opinion mining helps gather insights about how treatments are perceived and what concerns individuals have regarding their health.

People often share their thoughts about medical issues on social media, making it a rich source for gathering public opinion. However, this information is not always straightforward, as comments may contain implicit claims about health. For example, someone might say, "I felt better after trying this treatment," which implies they believe the treatment is effective without directly stating it.

#The Challenge of Analyzing Health Opinions

Analyzing health opinions online presents unique challenges. First, discussions can be complex and filled with jargon. Second, many comments may not explicitly state opinions but rather imply them. This requires a careful approach to identify and interpret these claims so that we can understand community sentiments.

Long COVID is particularly tricky because it involves ongoing symptoms and varying treatment approaches. As more people seek support and advice online, understanding these conversations becomes vital.

#Using LLMs for Opinion Mining

To better identify and understand opinions on long COVID treatments, we can use LLMs. These models have been trained on vast amounts of text data, allowing them to recognize patterns and meanings in language. With their robust capabilities, LLMs can help classify opinions expressed in online discussions.

The approach taken here involves two primary tasks: identifying health claims and assessing opinions about these claims. First, we need to see if a post includes a health-related claim. Then, we determine whether the comments are supportive, critical, or neutral regarding that claim.

#Creating a New Dataset

To train and evaluate LLMs on these tasks, we created a specific dataset called Long COVID-Stance. This dataset consists of posts and comments from a relevant Reddit community where people discuss their experiences with long COVID.

The dataset includes various types of claims: some are explicitly stated, while others are implied. By focusing on real conversations in an active community, we can capture a range of opinions and discussions about long COVID. This dataset will help evaluate how well LLMs can identify claims and gauge community sentiment.

#Methods of Data Collection

The process of gathering data involved extracting posts from Reddit over a certain timeframe. We focused on posts labeled as 'Research' or 'Article,' as these tend to contain information about long COVID backed by studies or news articles. After narrowing down the posts, we analyzed them to determine which contained health claims.

Once the relevant posts were selected, we looked at the comments made by users. The goal was to understand how these comments related to the claims made in the titles of the posts. We focused on the main comments rather than replies to ensure we captured the primary opinions of users.

#Claim Identification

Identifying whether a post contains a health claim is a crucial step in our analysis. A claim can be either explicit (clearly stated) or implicit (suggested or inferred). For example, saying, "I believe this treatment worked for me" is an explicit claim, while saying, "After trying this, I felt much better" is implicit.

To assist in identifying claims, we trained the LLMs to classify posts as containing a health claim or not. This step ensures that we are focusing on the most relevant posts for understanding opinions about long COVID treatments.

#Stance Detection

After identifying the claims, the next task is stance detection, which looks at how users feel about those claims. The main categories for stance detection are:

• In Favor: The user supports the claim.
• Against: The user criticizes or disagrees with the claim.
• Neutral: The user's comments do not show clear support or opposition.

By evaluating comments in relation to the identified claims, we can better understand how the community perceives various treatments and opinions.

#Evaluating LLMs

To gauge the effectiveness of LLMs in this context, we conducted experiments using different models, such as Llama2, GPT-3.5, and GPT-4. We evaluated their performance in two key areas: claim identification and stance detection.

In the claim identification phase, we focused on understanding how well each model could identify claims in the selected Reddit posts. For stance detection, we looked at how accurately the models could determine the stance expressed in the comments about the claims.

#Results of Claim Identification

The results showed that LLMs are quite effective at identifying both explicit and implicit claims. In our testing, GPT-3.5 performed the best among other models, demonstrating strong capabilities in recognizing claims present in the posts.

While our baseline model, ClaimDeBERTa, also performed decently, LLMs outperformed it by a notable margin. This indicates that LLMs, with their ability to draw on contextual information, can identify health claims more accurately in a domain-specific setting.

#Results of Stance Detection

For stance detection, we found that LLMs again outperformed traditional models. While earlier natural language inference (NLI) models struggled to correctly categorize stances, LLMs used in our experiments showed a significant improvement in accurately assessing users' opinions regarding long COVID claims.

GPT-4 stood out with the highest performance in stance detection, confirming that LLMs excel at evaluating nuanced comments in health discussions. The results indicate that LLMs can effectively process longer texts and manage the complexity often found in health-related discussions.

#Diversity of Opinions

During our analysis, we also noted the diversity of opinions present in the discussions about long COVID. Users expressed a wide range of views on different treatments, indicating that there is no one-size-fits-all approach to managing this condition.

Many comments reflected personal experiences, emphasizing the need for personalized care and the importance of individual responses to treatments. This highlights the value of analyzing online discussions, as they may uncover patterns in how different people cope with similar health challenges.

#Implications for Public Health

The insights gained from analyzing opinions about long COVID can have significant implications for public health. Understanding what treatments people believe work for them can guide healthcare providers in tailoring their recommendations and support for patients.

Moreover, by recognizing dangerous self-treatment options trending in online discussions, public health organizations can develop better education materials to inform individuals about safer practices.

#Future Directions

As we move forward, there are several avenues for further research. First, expanding the dataset beyond long COVID to include other health conditions can provide broader insights into health discussions online.

We also aim to explore ways to enhance the claim identification process, particularly focusing on extracting claims from the body of posts rather than just the titles. This would further enrich our dataset for analysis.

Improving the performance of smaller LLMs is another priority. While larger models have shown exceptional capabilities, finding ways to make smaller models effective in this context could make these tools more accessible for researchers and practitioners.

#Conclusion

In conclusion, LLMs offer a promising way to analyze emerging opinions in online health discourse. By focusing on long COVID discussions, we can gain valuable insights into public sentiment regarding treatments and experiences. Through ongoing efforts to refine data collection and analysis methods, we can further enhance our understanding of public opinions in health matters.

The use of LLMs in this field encourages a shift towards data-driven insights, enabling healthcare providers and researchers to better understand and address the needs of individuals facing complex health challenges. As we continue to analyze these conversations online, the hope is that we can inform and enhance personal and public health approaches moving forward.