Social media user abnormal psychological state identification method and system based on psychological scale

Abstract

The invention discloses a psychological scale-based social media user abnormal psychological state recognition method and system, relates to the technical field of artificial intelligence, and aims to solve the problem that the existing abnormal psychological recognition method performs abnormal psychological recognition only through basic understanding of posts and abnormal psychological states and cannot deeply mine posts closely related to the abnormal psychological states. According to the abnormal psychological state recognition method and device, the psychological scale is introduced to provide rich scale item information for abnormal psychological recognition, so that the recognition method can have more detailed and deep understanding on posts of a user, more accurate recognition can be made, and the recognition accuracy of abnormal psychological state recognition is improved. And the accuracy of abnormal psychological state recognition is improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method and system for identifying abnormal psychological states of social media users based on a psychological scale. Background Art

[0002] Social media data is huge, with strong social network relationships and a large number of posts. Sentiment analysis of social media can identify the abnormal psychological state of users when they send posts and classify the user's emotions. However, for sentiment analysis, the identification of abnormal psychological state is an important part of sentiment analysis, which affects the accuracy of sentiment analysis.

[0003] In the field of abnormal psychological state recognition, tasks are usually divided into two categories: post-level recognition and user-level recognition. Post-level recognition is the recognition of abnormal psychological states for a single post on social media; while user-level recognition is the comprehensive recognition of multiple posts by a user. Existing abnormal psychological recognition methods only perform abnormal psychological recognition based on a basic understanding of posts and abnormal psychological states, and fail to deeply explore posts that are closely related to abnormal psychological states. This leads to the problem of low accuracy in abnormal psychological state recognition. Summary of the invention

[0004] The purpose of the present invention is to address the existing abnormal psychology recognition methods, which only perform abnormal psychology recognition through a basic understanding of posts and abnormal psychological states, but fail to deeply explore posts closely related to abnormal psychological states, which leads to the problem of low accuracy in abnormal psychological state recognition. A method and system for identifying abnormal psychological states of social media users based on a psychological scale are proposed.

[0005] The technical solution adopted by the present invention to solve the above technical problems is:

[0006] The method for identifying abnormal psychological states of social media users based on a psychological scale includes the following steps:

[0007] Step 1: Determine whether the recognition task is a post-level task or a user-level task. The post-level task is to recognize abnormal psychological states for a single post on social media, and the user-level task is to recognize abnormal psychological states for multiple posts of a user.

[0008] For post-level tasks, a single post obtained from social media is directly input into the LLM model. For user-level tasks, all posts of the user are first obtained, and then the posts are screened according to the correlation between the posts and the scale items in the psychological scale. The screened posts are concatenated and used as the input of the LLM model.

[0009] Step 2: Introduce the psychological scale into the LLM model as a prompt basis, and conduct multiple rounds of inquiries to the LLM model based on the input of step 1 to obtain the symptoms in the scale items corresponding to the post, and obtain the abnormal psychological state identification results based on each abnormal psychological state and its corresponding symptoms.

[0010] Furthermore, in step 1, the posts are screened according to the correlation between the posts and the scale items in the psychological scale, and the screened posts are spliced ​​as the input of the LLM model in the specific steps of:

[0011] Step 1: Obtain the psychology scale and extract all scale items from the psychology scale;

[0012] Step 1 and 2: Paginate each post of the user i and each scale item s j are converted into post vectors V(p i ) and scale item vector V(s j );

[0013] Step 1-3: Calculate the cosine similarity between each post vector and each scale item vector to obtain the similarity score CosSim(p i ,s j ), expressed as:

[0014]

[0015] Step 14: For each post, select the highest similarity score as the integrated similarity score AggSim(p i );

[0016]

[0017] Among them, M is the total number of scale items, j is the jth scale item;

[0018] Step 15: Sort all posts according to the integrated similarity score, and select K posts according to the sorting, expressed as:

[0019] Top-KPosts=argmax K ({AggSim(p i )|p i ∈P})

[0020] Among them, the total number of tokens of K posts is less than the input upper limit of the LLM model, and Top-KPosts is the K posts with the highest integrated similarity scores. K Indicates selecting posts corresponding to the largest K values;

[0021] Step 16: After splicing the selected K posts, input them into the LLM model. The input is represented as:

[0022] Input User-level =Concat(Top-KPosts)

[0023] Among them, Input User-level It is the input of user-level tasks, and Concat means concatenation.

[0024] Furthermore, the small embedding model is paraphrase-MiniLM-L6-v2.

[0025] Furthermore, in step 2, the psychological scale is introduced into the LLM model as a prompt basis, and according to the input of step 1, multiple rounds of inquiries are conducted on the LLM model to obtain the symptoms in the scale items corresponding to the post. The specific steps are:

[0026] According to the input post, the LLM model is used to obtain the response r of the input post and each scale item. i , and then get the response set R corresponding to the input post, and get the symptoms in the scale items corresponding to the post according to the response set R. The response set R is expressed as:

[0027]

[0028] Among them, r i =0 means that the scale item s does not exist in the post i The symptoms reflected, and r i =1 indicates that the post contains scale item s i The symptoms reflected, i∈{1,2,...,N}, N represents the total number of scale items.

[0029] Furthermore, the abnormal psychological state recognition result is expressed as:

[0030]

[0031] Where (p, R) is a single post p and its reply R, (Top-K Posts, R) is the most relevant K posts and their reply R, Identify results for abnormal mental states.

[0032] A social media user abnormal psychological state identification system based on a psychological scale, the system comprising a task identification module, an inquiry module and an identification module;

[0033] The task identification module is used to determine whether the identification task is a post-level task or a user-level task. The post-level task is to identify abnormal psychological states for a single post on social media, and the user-level task is to identify abnormal psychological states for multiple posts of a user.

[0034] For post-level tasks, a single post obtained from social media is directly input into the LLM model. For user-level tasks, all posts of the user are first obtained, and then the posts are screened according to the correlation between the posts and the scale items in the psychological scale. The screened posts are concatenated and used as the input of the LLM model.

[0035] The inquiry module is used to introduce the psychology scale into the LLM model as a prompt basis, and according to the input of the LLM model, conduct multiple rounds of inquiries to the LLM model to obtain the symptoms in the scale items corresponding to the post, and obtain the abnormal psychological state identification results according to each abnormal psychological state and its corresponding symptoms.

[0036] Furthermore, the task identification module screens posts according to the relevance between the posts and the scale items in the psychological scale, and concatenates the screened posts as the input of the LLM model. The specific steps are as follows:

[0037] Step 1: Obtain the psychology scale and extract all scale items from the psychology scale;

[0038] Step 1 and 2: Paginate each post of the user i and each scale item s j are converted into post vectors V(p i ) and scale item vector V(s j );

[0039] Step 1-3: Calculate the cosine similarity between each post vector and each scale item vector to obtain the similarity score CosSim(p i ,s j ), expressed as:

[0040]

[0041] Step 14: For each post, select the highest similarity score as the integrated similarity score AggSim(p i );

[0042]

[0043] Among them, M is the total number of scale items, j is the jth scale item;

[0044] Step 15: Sort all posts according to the integrated similarity score, and select K posts according to the sorting, expressed as:

[0045] Top-KPosts=argmax K ({AggSim(p i )|p i ∈P})

[0046] Among them, the total number of tokens of K posts is less than the input upper limit of the LLM model, and Top-KPosts is the K posts with the highest integrated similarity scores. K Indicates selecting posts corresponding to the largest K values;

[0047] Step 16: After splicing the selected K posts, input them into the LLM model, expressed as:

[0048] Input User-level =Concat(Top-KPosts)

[0049] Among them, Input User-level It is the input of user-level tasks, and Concat means concatenation.

[0050] Furthermore, the small embedding model is paraphrase-MiniLM-L6-v2.

[0051] Furthermore, the inquiry module specifically performs the following steps:

[0052] According to the input post, the LLM model is used to obtain the response r of the input post and each scale item. i , and then get the reply set R corresponding to the input post, and get the symptoms in the scale items corresponding to the post according to the reply set R. The reply set R is expressed as:

[0053]

[0054] Among them, r i =0 means that the scale item s does not exist in the post i The symptoms reflected, and r i =1 indicates that the post contains scale item s i The symptoms reflected, i∈{1,2,...,N}, N represents the total number of scale items.

[0055] Furthermore, the abnormal psychological state recognition result is expressed as:

[0056]

[0057] Where (p, R) is a single post p and its reply R, (Top-K Posts, R) is the most relevant K posts and their reply R, Identify results for abnormal mental states.

[0058] The beneficial effects of the present invention are:

[0059] This application provides rich scale item information for abnormal psychological identification by introducing psychological scales, so that the identification method can have a more detailed and in-depth understanding of the user's posts, thereby making more accurate identifications, thereby improving the accuracy of abnormal psychological state identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is the overall architecture diagram of this application. DETAILED DESCRIPTION

[0061] It should be particularly noted that, in the absence of conflict, the various embodiments disclosed in this application can be combined with each other.

[0062] Specific implementation method 1: refer to Figure 1 Specifically describing this embodiment, the method for identifying abnormal psychological states of social media users based on a psychological scale described in this embodiment includes the following steps:

[0063] Step 1: Determine whether the recognition task is a post-level task or a user-level task. The post-level task is to recognize abnormal psychological states for a single post on social media, and the user-level task is to recognize abnormal psychological states for multiple posts of a user.

[0064] For post-level tasks, a single post obtained from social media is directly input into the LLM model. For user-level tasks, all posts of the user are first obtained, and then the posts are screened according to the correlation between the posts and the scale items in the psychological scale. The screened posts are spliced ​​and input into the LLM model.

[0065] Step 2: Introduce the psychological scale into the LLM model as a prompt basis, and conduct multiple rounds of inquiries to the LLM model based on the input of step 1 to obtain the symptoms in the scale items corresponding to the post;

[0066] Step 3: Input the symptoms obtained in step 2 and the input in step 1 into the LLM model together to obtain the output of abnormal mental state recognition results.

[0067] In step 1, the posts are screened according to their relevance to the scale items in the psychological scale, and the screened posts are spliced ​​and input into the LLM model. The specific steps are as follows:

[0068] Step 1: Get the psychology scale and extract all the scale items from the psychology scale, that is, all N scale items S = {s 1 ,s 2 ,…s N};

[0069] Step 1 and 2: Define a single post of the post-level task as p, and define the task goal as identifying the abnormal psychological state reflected in post p Define the post set of a user U in the user-level task as P = {p 1 ,p 2 ,…,p L}, L is the number of posts, then the task goal is to identify the overall abnormal psychological state of the user based on the post set P

[0070] Each post of the user i and each scale item s j are converted into post vectors V(p i ) and scale item vector V(s j );

[0071] Step 1-3: Calculate the cosine similarity between each post vector and each scale item vector to obtain the similarity score CosSim(p i ,s j ), expressed as:

[0072]

[0073] Step 14: For each post, select the highest similarity score as the integrated similarity score AggSim(p i );

[0074]

[0075] Among them, M is the total number of scale items, j is the jth scale item;

[0076] Step 15: Sort all posts according to the integrated similarity score, and select K posts according to the sorting, expressed as:

[0077] Top-KPosts=argmax K ({AggSim(p i )| p i∈P})

[0078] Among them, the total number of tokens of K posts is less than the input upper limit of the LLM model, and Top-KPosts is the K posts with the highest integrated similarity scores. KIndicates selecting posts corresponding to the largest K values;

[0079] Step 16: After splicing the selected K posts, input them into the LLM model, expressed as:

[0080] Input User-level =Concat(Top-KPosts)

[0081] Among them, Input User-level It is the input of user-level tasks, and Concat means concatenation.

[0082] The small embedding model is paraphrase-MiniLM-L6-v2.

[0083] The specific steps of step 2 are:

[0084] According to the input post, the LLM model is used to obtain the response r of the input post and each scale item. i , and then get the reply set R corresponding to the input post, expressed as:

[0085]

[0086] Among them, r i =0 means that the scale item s does not exist in the post i The symptoms reflected, and r i =1 indicates that the post contains scale item s i The symptoms reflected, i∈{1,2,...,N}, N represents the total number of scale items.

[0087] During the specific implementation, the task requirements of identifying abnormal mental states are input to the LLM, for example: "You are an AI assistant specializing in text analysis, and I am a human. This application will complete a text analysis task through multiple rounds of dialogue. The task is as follows: This application has posts written by users. In each round of dialogue, I will give you a statement about the author. Based on the author's text, you need to score this statement. After scoring all the statements, I will ask you which level of suicide risk you think the author is more likely to be at, and then you need to give your choice." And explain the scoring format for each round: the score is 0 or 1, where 0 means disagreement and 1 means agreement.

[0088] The psychological scale is used as a prompt basis to collect symptoms. In order to maximize the use of scale items, save tokens and collection time, this application selects the 6 scale items with the highest relevance to the current post from all N scale items S as the prompt basis. The selection method is similar to the above content, that is, the cosine similarity between the scale item and the current post is calculated, and the 6 scale items with the highest similarity are selected. Subsequently, this application adopts the method of prompting item by item. In the i-th round of inquiry, scale item si Used to ask if the symptom exists in the LLM post.

[0089] In step three, the LLM response set R is submitted to the LLM together with the user posts, and the subdivision definition of each abnormal mental state classification is introduced to the LLM.

[0090] For example, in the risk assessment of the abnormal psychological state of suicide, this application will define five suicide risk levels based on the original five categories of the CSSRS suicide risk scale, which are (ranked from small to large):

[0091] Support(SU) <Indicator(IN)<Ideation(ID)<Behavior(BR)<Attempt(AT)

[0092] 1) SU: (Suicide Support) does not show any risk factors and does not include any suicidal thoughts or ideas.

[0093] 2) IN: (Suicide Indicator) shows some risk factors that may lead to suicide.

[0094] 3) ID: (Suicide Ideation) Having thoughts about death or suicide, but without any actual behavior.

[0095] 4) BR: (Suicide Behavior) Attempting suicide or dangerous behavior that may lead to death, or may be preparing to implement a suicide plan.

[0096] 5) AT; (Suicide Attempt) Any suicide attempt has ever occurred, regardless of whether it resulted in harm.

[0097] Finally, the definitions of each category are input into the LLM.

[0098] Final prediction of abnormal psychological state using LLM:

[0099]

[0100] Among them, in the post-level task, LLM analyzes a single post p and reply R, while in the user-level task, LLM analyzes the most relevant K posts and R. Finally, the recognition result of abnormal psychological state is obtained

[0101] This application is applied to abnormal psychological states at the user level or post level on social media platforms (such as Twitter and Reddit), and identifies various abnormal psychological states such as depression, suicidal state, anxiety, etc. The thought chain prompt method based on the psychology scale applied by this agency can directly call the large language model API (such as GPT-3.5-turbo) for deployment, eliminating the model training link and requiring no GPU resources, which is very convenient.

[0102] This application uses the scale corresponding to abnormal psychological state as the prompt basis. For example, for depression state identification, the PHQ-9 depression scale can be selected; for suicide state identification, the C-SSRS suicide risk assessment scale can be selected; for anxiety state identification, the BAI anxiety scale can be used. Extract key scale items from the scale, prompt LLM to collect relevant symptoms, and identify abnormal psychological states.

[0103] This application selected four social media datasets corresponding to different abnormal psychological states for experiments. The information of these four datasets is shown in Table 1:

[0104]

[0105] Table 1 Statistical information of four data sets

[0106] Among them, DR and Dreaddit are post-level datasets, which are social media datasets for identifying depression and anxiety states, respectively. The evaluation indicator is the weighted F1 score. CSSRS and eRisk 2018 are user-level datasets, which are aimed at identifying suicidal states and depressive states, respectively. The evaluation indicator of CSSRS is grade F1 score, and the evaluation indicator of eRisk2018 is binary F1 score.

[0107] The proposed hinting method is compared with the following six existing advanced hinting methods:

[0108] (1) V_sen: Use the VADER dictionary to assign sentiment polarity labels to posts. Then design prompts by adding sentiment polarity labels to zero-shot prompts.

[0109] (2) N_sen: Use the NRC EmoLex dictionary to assign sentiment polarity labels to posts. Then design prompts by adding sentiment polarity labels to zero-shot prompts.

[0110] (3) N_emo: Use the NRC EmoLex dictionary to assign sentiment labels to posts. Then design prompts by adding sentiment labels to zero-shot prompts.

[0111] (4) CoT_ZS: Enhances zero-shot prompts by introducing a thought chain component to guide the LLM to generate a step-by-step explanation of its decision.

[0112] (5) CoT_emo_ZS: Integrates the emotion enhancement component and thought chain component into zero-shot prompts to help LLM pay attention to emotional cues and generate a detailed step-by-step explanation of its decision-making process.

[0113] (6) CoT_emo_FS: Based on the CoT_emo_ZS method, it guides LLM to make more standard predictions by adding response examples written by domain experts.

[0114] Some of these methods consider the relationship between emotions and abnormal psychological states and provide some examples of expert annotations. However, only the method of this application incorporates professional psychological scales into the prompt design. For different abnormal situations, this application uses the corresponding scale as the basis for zero-sample prompts. For depressive state recognition, this application uses the PHQ-9 depression scale, which has 9 items to assess the severity of depression. For anxiety state recognition, this application uses the BAI scale, which consists of 21 items. However, using all 21 scale items will generate too many query rounds and consume a large number of tokens. Moreover, many of these scale items are completely irrelevant to the content of the post and can be omitted directly. Therefore, this application selects 9 scale items from BAI to prompt LLM based on the relevance of the scale items to the post. For suicidal state recognition, this application uses the C-SSRS scale, which consists of 6 items and aims to assess the severity of suicide risk by exploring suicidal signs, suicidal thoughts, suicidal plans, and suicidal behaviors.

[0115] In the prompt experiment, this application selects GPT-3.5-turbo for the experiment. The comparison of the results of this application's method and six baseline methods is shown in Table 2:

[0116]

[0117] Table 2 Main experimental results (Scale_CoT_ZS represents the method of this application, and the best results are highlighted in bold)

[0118] The proposed hinting method based on the psychological scale achieved the best performance on all four datasets, showing a significant performance improvement. Previous hinting methods only provided a basic understanding of posts and abnormal psychological states, and failed to delve into posts that were closely related to abnormal psychological states. This resulted in the LLM lacking a reliable basis for prediction, and ultimately led to incorrect predictions. In contrast, the introduction of the psychological scale provides LLM's predictions with rich scale item information, allowing the model to have a more detailed and in-depth understanding of the user's posts, and thus to make more accurate identifications.

[0119] It is worth noting that even in the zero-sample setting, the method of the present application is significantly better than the few-sample CoT method CoT_emo_FS. The present application believes that although previous few-sample CoT methods have combined examples written by experts as guidance, LLMs often have difficulty understanding the deeper reasoning logic contained in these examples. Therefore, the model fails to grasp the key points of predicting abnormal psychological states. In contrast, the method of the present application collects symptoms through psychological scales, providing LLMs with detailed and theoretically supported reasoning basis, enabling the model to make correct inferences.

[0120] In addition, the method of the present application is also superior to all emotion enhancement methods. Although these methods combine emotion-related information, they are too simple to reveal deeper emotional information, whether adding emotion labels or letting the model focus on analyzing the user's emotions. This application believes that although emotional information can reflect certain aspects of the user's psychological state and may reveal certain signs of abnormal psychological state, it cannot capture all the manifestations required to identify abnormal psychological states. Emotional information usually cannot reflect physical symptoms (such as abdominal pain or insomnia), deeper psychological states (such as guilt or jealousy), and certain objective factors (such as criticism from others), and therefore lacks many key elements necessary to identify abnormal psychological states. The method of this application comprehensively collects this information through a scale, providing a solid foundation for prediction.

[0121] At present, the research on prompt engineering based on large models has become an important trend in the field of artificial intelligence, and is widely used in many fields such as natural language processing and image recognition. The universality of this type of work is reflected in its significant improvement in the performance and adaptability of large language models (LLMs). By precisely designing prompts, researchers can guide the model to better understand the task requirements, thereby generating more accurate and professional results. This not only promotes technological progress, but also provides new ideas and methods for practical applications, especially in fields such as education, medical care, and customer service. Optimized prompt engineering can significantly improve interaction efficiency and user experience.

[0122] In this trend, this application introduces psychological scales for the identification of abnormal psychological states, focusing on the prompt design based on the psychological scale and the construction of the entire process, which not only improves the efficiency of LLM use, but also ensures its scientificity and professionalism in mental health assessment. By combining psychological scales with LLM, this application can systematically collect and analyze user data to provide a quantitative basis for the identification of abnormal psychological states. This innovative idea optimizes the application scenarios of large models and provides new solutions for the field of mental health, demonstrating how to effectively use this powerful tool to promote the accuracy and interpretability of mental health assessments.

[0123] It should be noted that the specific implementation is only an explanation and description of the technical solution of the present invention, and cannot be used to limit the scope of protection of the rights. Any partial changes made according to the claims and description of the present invention should still fall within the scope of protection of the present invention.

Claims

1. A method for identifying abnormal psychological states of social media users based on a psychological scale, characterized by The following steps are involved: Step 1: Determine whether the recognition task is a post-level task or a user-level task. The post-level task is to recognize abnormal psychological states for a single post on social media, and the user-level task is to recognize abnormal psychological states for multiple posts of a user. For post-level tasks, a single post obtained from social media is directly input into the LLM model. For user-level tasks, all posts of the user are first obtained, and then the posts are screened according to the correlation between the posts and the scale items in the psychological scale. The screened posts are concatenated and used as the input of the LLM model. Step 2: Introduce the psychological scale into the LLM model as a prompt basis, and conduct multiple rounds of inquiries to the LLM model based on the input of step 1 to obtain the symptoms in the scale items corresponding to the post, and obtain the abnormal psychological state identification results based on each abnormal psychological state and its corresponding symptoms.

2. The method for identifying abnormal psychological states of social media users based on a psychological scale according to claim 1 is characterized in that In step 1, the posts are screened according to the correlation between the posts and the scale items in the psychological scale, and the screened posts are spliced ​​as the input of the LLM model. The specific steps are: Step 1: Obtain the psychology scale and extract all scale items from the psychology scale; Step 1 and 2: Paginate each post of the user i and each scale item s j are converted into post vectors V(p i ) and scale item vector V(s j ); Step 1-3: Calculate the cosine similarity between each post vector and each scale item vector to obtain the similarity score CosSim(p i ,s j ), expressed as: Step 14: For each post, select the highest similarity score as the integrated similarity score AggSim(p i ); Among them, M is the total number of scale items, j is the jth scale item; Step 15: Sort all posts according to the integrated similarity score, and select K posts according to the sorting, expressed as: Top-KPosts=argmax K ({AggSim(p i )|p i ∈P}) Among them, the total number of tokens of K posts is less than the input upper limit of the LLM model, and Top-KPosts is the K posts with the highest integrated similarity scores. K Indicates selecting posts corresponding to the largest K values; Step 16: After splicing the selected K posts, input them into the LLM model. The input is represented as: Input User-level =Concat(Top-KPosts) Among them, Input User-level It is the input of user-level tasks, and Concat means concatenation.

3. The method for identifying abnormal psychological states of social media users based on a psychological scale according to claim 2 is characterized in that The small embedding model is paraphrase-MiniLM-L6-v2.

4. The method for identifying abnormal psychological states of social media users based on a psychological scale according to claim 3 is characterized in that In step 2, the psychological scale is introduced into the LLM model as a prompt basis, and according to the input of step 1, multiple rounds of inquiries are conducted on the LLM model to obtain the symptoms in the scale items corresponding to the post. The specific steps are: According to the input post, the LLM model is used to obtain the response r of the input post and each scale item. i , and then get the reply set R corresponding to the input post, and get the symptoms in the scale items corresponding to the post according to the reply set R. The reply set R is expressed as: Among them, r i =0 means that the scale item s does not exist in the post i The symptoms reflected, and r i =1 indicates that the post contains scale item s i The symptoms reflected, i∈{1,2,...,N}, N represents the total number of scale items.

5. The method for identifying abnormal psychological states of social media users based on a psychological scale according to claim 4 is characterized in that The abnormal psychological state recognition result is expressed as: Among them, (p,R) is a single post p and reply R, (Top-KPosts,R) is the most relevant K posts and reply R, Identify results for abnormal mental states.

6. A social media user abnormal psychological state recognition system based on a psychological scale, characterized by The system includes a task identification module, an inquiry module and an identification module; The task identification module is used to determine whether the identification task is a post-level task or a user-level task. The post-level task is to identify abnormal psychological states for a single post on social media, and the user-level task is to identify abnormal psychological states for multiple posts of a user. For post-level tasks, a single post obtained from social media is directly input into the LLM model. For user-level tasks, all posts of the user are first obtained, and then the posts are screened according to the correlation between the posts and the scale items in the psychological scale. The screened posts are concatenated and used as the input of the LLM model. The inquiry module is used to introduce the psychology scale into the LLM model as a prompt basis, and according to the input of the LLM model, conduct multiple rounds of inquiries to the LLM model to obtain the symptoms in the scale items corresponding to the post, and obtain the abnormal psychological state identification results according to each abnormal psychological state and its corresponding symptoms.

7. The social media user abnormal psychological state identification system based on the psychological scale according to claim 6 is characterized in that The specific steps of screening posts according to the relevance between posts and scale items in the psychological scale and splicing the screened posts as input of the LLM model are as follows: Step 1: Obtain the psychology scale and extract all scale items from the psychology scale; Step 1 and 2: Paginate each post of the user i and each scale item s j are converted into post vectors V(p i ) and scale item vector V(s j ); Step 1-3: Calculate the cosine similarity between each post vector and each scale item vector to obtain the similarity score CosSim(p i ,s j ), expressed as: Step 14: For each post, select the highest similarity score as the integrated similarity score AggSim(p i ); Among them, M is the total number of scale items, j is the jth scale item; Step 15: Sort all posts according to the integrated similarity score, and select K posts according to the sorting, expressed as: Top-KPosts=argmax K ({AggSim(p i )|p i ∈P}) Among them, the total number of tokens of K posts is less than the input upper limit of the LLM model, and Top-KPosts is the K posts with the highest integrated similarity scores. K Indicates selecting posts corresponding to the largest K values; Step 16: After splicing the selected K posts, input them into the LLM model, expressed as: Input User-level =Concat(Top-KPosts) Among them, Input User-level It is the input of user-level tasks, and Concat means concatenation.

8. The social media user abnormal psychological state identification system based on the psychological scale according to claim 7 is characterized in that The small embedding model is paraphrase-MiniLM-L6-v2.

9. The social media user abnormal psychological state identification system based on the psychological scale according to claim 8 is characterized in that The query module specifically performs the following steps: According to the input post, the LLM model is used to obtain the response r of the input post and each scale item. i , and then get the reply set R corresponding to the input post, and get the symptoms in the scale items corresponding to the post according to the reply set R. The reply set R is expressed as: Among them, r i =0 means that the scale item s does not exist in the post i The symptoms reflected, and r i =1 indicates that the post contains scale item s i The symptoms reflected, i∈{1,2,...,N}, N represents the total number of scale items.

10. The social media user abnormal psychological state identification system based on the psychological scale according to claim 9 is characterized in that The abnormal psychological state recognition result is expressed as: Where (p, R) is a single post p and its reply R, (Top-K Posts, R) is the most relevant K posts and their reply R, Identify results for abnormal mental states.

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