Using Large Language Models ( LLM s) to Extract Evidence from Pre-Annotated Social Media Data (2024)

Abstract: For numerous years, researchers have employed social media data to gain insights into users’ mental health. Nevertheless, the majority of investigations concentrate on categorizing users into those experiencing depression and those considered healthy, or on detection of suicidal thoughts. In this paper, we aim to extract evidence of a pre-assigned gold label. We used a suicidality dataset containing Reddit posts labeled with the suicide risk level. The task is to use Large Language Models (LLMs) to extract evidence from the post that justifies the given label. We used Meta Llama 7b and lexicons for solving the task and we achieved a precision of 0.96.

## introduction in today's world, many people use social media platforms. these platforms allow individuals to express themselves openly, sharing daily details about their activities and thoughts. researchers have been studying social media data for years to understand users' mental health. natural language processing (nlp) is often applied to social media data in research that focuses on classifying the presence or absence of depression @xcite . researchers also examine how to detect the transition from depression to suicidal ideation @xcite @xcite . in this paper, we explain our approach to the clpsych 2024 shared task. the goal of this shared task is to utilize large language models (llms) to detect textual cues that support the designated suicide risk level, which may be classified as low, moderate, or high. this "evidence" could be provided in two ways, either highlighting (or "extracting") relevant spans within the text or by providing a summary, aggregating evidence that supports the assigned suicide risk level. ## dataset data used for this shared task was from @xcite . it was pulled from reddit. this well-known social media platform contains communities known as "subreddits", each of which covers a different topic. access has been granted to the umd suicidality dataset v2, encompassing multiple reddit users and their corresponding posts on the platform, along with the associated suicide risk level labels. the dataset incorporates annotations for suicide risk levels across subsets of posts within the r/suicidewatch subreddit, categorized as follows: this shared task exclusively concentrates on the assessment of low, moderate, and high risk levels. it is essential to note that, although the term "suicidal crisis" was not employed in the original risk labeling by @xcite , the high category closely aligns with this concept, denoting an acute situation necessitating immediate intervention. all authors have signed the data user agreement (as requested by the organisers) to have access to the dataset. ## methods in this section, we will describe the methods we developed to address the shared tasks. the main instruction for this task was to use large language models (llms) to extract the evidence. llms have demonstrated superior performance in understanding human language and generating text resembling it. ## evaluation as per task organizers @xcite , submissions are evaluated against a test set annotated by two domain experts. each test set example comprises (i) the risk level label of an individual, (ii) a list of posts written by the individual, (iii) text spans highlighted by annotators from the posts with evidence that support the risk level label, and (iv) a human-written summary that aggregates the highlighted evidence and observations into a single piece of text. evaluation metrics are as follows: summarized evidence at a user-level, the organaizer score each sentence in the submitted evidence summary by running an nli model on it (hypothesis) and every gold summary sentence (premise), taking the maximum contradiction probability. then, average across all submitted sentences. overall submission-level score is the mean contradiction score across all users. ## results we applied prompts to llama, and it responded to the majority of them with an explanation for the given post. although large language models (llms) are known for their robust language processing capabilities, they have encountered difficulties in addressing specific aspects of mental health. in some cases, llama refuses to answer and responds with "it is important to note that this is just one text message, and it is not possible to make a definitive assessment of the person's suicide risk level based on this one message" or "it is important to note that these are just a few potential indicators of suicide risk, and that each person's situation is unique. however, if you are concerned about someone's safety, it is important to take their concerns seriously and offer support and resources.." the prompt that provides us with the best matching summary evidence for all of the posts was "can you let me know in a paragraph why this text is considered low mood?" as it scored 0.964 consistency with the gold standard, where the prompts "explain why the user who wrote this text has [suicide level] suicide risk level" and "write a paragraph on why this text might contain [suicide level] suicide risk" scored 0.873 and 0.878, respectively. in this shared task, it was imperative to extract direct quotes, or highlights, from the text of posts. our model adeptly performed this task while also rectifying any spelling mistakes within these quotes. however, during the subsequent validity check phase before submission, this spell-checking process inadvertently led to errors, resulting in some posts being submitted with empty quotes due to the approaching deadline. consequently, this issue contributed to a lower overall recall (0.577). nevertheless, it's worth noting that our model maintained a high level of recall (0.887) for all posts that did not contain empty quotes. the incorporation of a lexicon in our work enhances the results by expanding the list of highlights, identified during the extraction process. this integration contributes to an increased recall rate as the lexicon serves as a valuable reference, allowing the model to recognize and include additional relevant quotes that align with predefined criteria. by leveraging the lexicon, our approach not only captures a broader spectrum of highlights but also augments the comprehensiveness of the extracted information in the summary, thereby improving the overall performance of the system. the results obtained using our proposed method are illustrated in in conclusion, researchers have extensively utilized social media data over the years to gain valuable insights into users' mental health. however, the predominant focus of many investigations has been on categorizing users into those with depression and those deemed healthy, or on detecting suicidal ideation. in this study, our objective was to extract evidence corresponding to pre-assigned gold labels. to achieve this, we utilized a suicidality dataset comprising reddit posts labeled with suicide risk levels. our task involved employing large lan-guage models (llms) to extract evidence from the posts justifying the assigned labels. through the utilization of meta llama 7b and lexicons, we attained commendable results, achieving a precision rate of 0.96. these findings underscore the efficacy of utilizing advanced language models and lexicon-based approaches in extracting evidence pertinent to the assigned suicide risk levels from social media posts. in the future, we aim to try other llm models such as openai gpt and google bard. we also aim to expand the suicidal words list and extract additional features from the text that could enhance the obtained results. ## limitations llama2 7b is limited to handling a maximum of 4096 tokens, which resulted in trimmed posts, leading to potential information loss and truncation of longer posts that possibly caused incomplete understanding and biased sampling. in addition, the llama2 model used in this paper is trained using extensive datasets collected from the internet. although it can produce human-like text, it is worth noting that training it on domain-specific data (mental health data) could improve its performance. ## ethical consideration the collected dataset contains only publicly available posts from reddit, and we have signed a data user agreement not to share or distribute any data outside the team. we are also committed to following ethical practices to protect users' privacy and anonymity. this includes not using commercial llms to protect user privacy, not submitting all or part of the data to any platform that may use the data for training, and data is only stored on password protected servers and computers.