A multi-agent based social platform robot detection method

Abstract

The application relates to a kind of social platform robot detection methods based on multi-agent, comprising: creating social platform API connection instance to collect target user comment history data to obtain original data set;Build the orchestrator-multi-agent collaborative detection model driven by large language model;Data processing agent constructs comment level relationship to input data, and carries out cleaning, standardization and quality detection, forms the structured data set after preprocessing and writes into shared memory;Feature engineering agent reads structured data from shared memory, calculates user-level multi-dimensional behavior feature vector and writes into shared memory;Rule discrimination agent reads feature data, executes rule discrimination, judges whether feature hits piece by piece, thereby outputs rule risk score and writes into shared memory;Modeling agent reads feature data, trains anomaly detection and supervised classification model, executes multi-model fusion to obtain robot risk determination result and writes into shared memory;Explanation generation agent reads model detection result and rule data, and fusion outputs the final interpretable social robot detection result.The application reduces the dependence on single rule or single model by adopting multi-agent cooperation to complete data processing, feature construction, rule discrimination and multi-model fusion detection, greatly improves the accuracy and robustness of social platform robot detection.

Description

Technical Field

[0001] This invention relates to the fields of data mining, artificial intelligence, and social robot detection, and more specifically to a method for detecting social platform robots based on multi-agent systems. Background Technology

[0002] With the development of internet technology, social media has become a platform for communication, information exchange, and news dissemination among a large audience. Consequently, the user base of major social media platforms has experienced exponential growth, with billions using online social networks for information dissemination, entertainment, and social interaction. Simultaneously, with the rapid development of artificial intelligence (AI) technology, a large number of AI-controlled social bots have emerged and are active on various social media platforms. They mimic the behavior of real users, delivering information and interacting with them. Compared to human users, social bots can respond to information quickly, automatically disseminate and distribute content, achieving more efficient information delivery. They can not only automatically send news and push real-time hot topics, but also make precise recommendations based on user interests, improving information reach efficiency. However, while these technologies have brought many benefits to society, they have also led to malicious operations, such as spreading misinformation and deploying fake accounts to guide public opinion and manipulate users. Research shows that social bots only need to occupy a small portion of a given discussion to change public opinion, thus making their views the dominant ones. Therefore, social bots exhibiting malicious behavior have become a significant factor influencing public opinion, posing a serious security threat to individuals, organizations, and even nations. How to analyze and detect social bots in these complex social networks has become an important research topic.

[0003] Early research on social bot detection often employed single-model or single-process detection mechanisms. These primarily relied on manually constructed features or text analysis. For example, the algorithm in bulletin CN115309813A (a social bot detection algorithm based on user semantics, attributes, and neighborhood information) first semantically encodes user-posted content, normalizes and embeds attribute features, and encodes neighborhood / interaction structures. These three representations are then concatenated and fused into a user vector. Subsequently, pseudo-labels are generated using density-based clustering, and sample augmentation is performed using a conditional generative adversarial network. A graph representation learning model is then used to learn node representations, and finally, HDBSCAN clustering is used to output the bot or human result. While this method fully utilizes neighborhood information, it still operates on a single-process mechanism from vectorized representation to single-path training and then to clustering discrimination. It is highly dependent on the quality of the text and manually defined attributes and neighborhood relationships, exhibiting significant limitations. Announcement No. CN120045967A (A Social Robot Detection Method and System Based on Tweet Filtering) constructs a labeled user text sample library based on user tweets, user profiles, and user attributes. The tweet texts are cleaned and sampled to a uniform length. A tweet-level classifier is then trained to filter user tweets. The filtered tweet representations are then fused with user profile and attribute features to train a user-level classification model that outputs the robot judgment result. This method fully utilizes user text information. However, with the development of generative AI technology, many social robots can generate high-quality text that conforms to human language habits, making detection methods relying solely on text features increasingly ineffective when dealing with new types of robots.

[0004] In summary, existing methods typically treat the detection process as a single-model discrimination task, lacking the ability to decompose and collaboratively process complex detection workflows. For different detection dimensions such as text semantic analysis, behavioral pattern modeling, and network structure analysis, they often employ independent modeling or simple feature concatenation, making it difficult to achieve collaborative reasoning and dynamic decision-making among multi-dimensional information. How to effectively detect social robots and combine them with large-scale models for intelligent analysis, improving detection accuracy and interpretability through multi-agent collaboration, remains a challenge for researchers. Summary of the Invention

[0005] To overcome the shortcomings and drawbacks of existing methods, this invention proposes a multi-agent-based method for detecting social platform robots. This invention collects user comment data through social platform APIs, utilizes a multi-agent collaborative architecture for data processing, feature engineering, rule mining, and model training, and combines this with a large language model for intelligent analysis, thereby achieving high-precision detection and interpretable analysis results for social robots on social platforms.

[0006] The technical solution adopted in this invention is a method for detecting social platform robots based on multi-agent intelligence. It includes the following steps: S1: Create a social platform API connection instance to collect historical comment data from target users and obtain the raw dataset; S2: Construct an orchestrator-multi-agent collaborative detection model driven by a large language model; the structure of the orchestrator-multi-agent collaborative detection model is as follows: a multi-agent orchestrator, a multi-agent collaborative detection module, a task input terminal, and a result output terminal. The multi-agent collaborative detection module includes shared memory and five specialized agents that are communicatively connected to the shared memory. The orchestrator is connected to the multi-agent collaborative detection module and calls the multi-agent agents as needed according to instructions. The five specialized agents include a data processing agent, a feature engineering agent, a rule discrimination agent, a modeling agent, and an interpretation and generation agent; the multi-agent orchestrator performs dynamic planning and scheduling based on a large language model, and is used to sequentially call the five specialized agents and output the social robot detection results; the shared memory is used to realize cross-agent data exchange and state synchronization; S3: The data processing agent constructs a hierarchical relationship of comments on the input data, and performs cleaning, standardization, and quality checks to form a preprocessed structured dataset. Write to shared memory; S4: The feature engineering agent reads structured data from shared memory and calculates user-level multidimensional behavioral feature vectors. And write it to shared memory; S5: The rule-determining agent reads feature data, performs rule determination, judges whether each feature is matched, and outputs the rule risk score and writes it to shared memory; S6: The modeling agent reads feature data, trains anomaly detection and supervised classification models, performs multi-model fusion to obtain robot risk assessment results and writes them into shared memory; S7: Interpret and generate intelligent agents that read model detection results and rule data, and fuse them to output the final interpretable social robot detection results.

[0007] Specifically, step S1 includes the following steps: S1.1: Create an authorized social platform API connection instance and call the interface to obtain target users. Comment history ,in The total number of comments for this user; S1.2: Extract core fields for each comment record, including the comment's unique identifier, the ID of the post to which it belongs, the parent comment, the comment author, the comment text, the creation time, the comment score, and the sub-forum name, and further obtain detailed post information based on the ID of the post to which it belongs; S1.3: Implement rate control and exponential backoff retry mechanisms when crawling data. Traffic control is performed by checking the remaining number of API requests. Let the remaining number of API requests be... The threshold is ,when It automatically sleeps and waits; when a network anomaly is encountered, it retryes exponentially according to the initial waiting time, up to the [number]th [time]. The waiting time for the next retry is:

[0008] in This is the initial waiting time. It is a random disturbance term, and the maximum number of retries is ; In step S2, the orchestrator-multi-agent collaborative detection model further includes five specialized agents: a data processing agent, a feature engineering agent, a rule discrimination agent, a modeling agent, and an interpretation and generation agent. Each agent employs a "reasoning-execution-observation" loop mechanism: first, the large language model analyzes the current context to make reasoning decisions; then, the selected tool is executed to complete the specific operation; finally, the execution result is observed and the context state is updated. The shared memory uses a namespace isolation mechanism, divided into five namespaces: data area, feature area, rule area, model area, and interpretation area. It supports key-value storage and historical record tracking, enabling cross-agent data exchange and state synchronization. Step S3 specifically includes the following steps: S3.1: After the data processing agent reads the data loading results, it uses the parent comment data to construct the comment hierarchy. Then, the large language model infers and decides to call the data cleaning tool to perform data cleaning and standardization processing, including deleting completely duplicate rows, removing records with missing necessary fields, filtering deleted user and automatic review robot records, cleaning whitespace characters from text fields, converting timestamps to standard datetime format and sorting them by time, and filtering low-activity users with fewer than 5 comments to ensure the effectiveness of feature calculation. S3.2: The data processing agent generates data quality description information and data structure definition, and processes the cleaned structured data. Write it to the shared data area for downstream intelligent agents to read and use; Step S4 specifically includes the following steps: S4.1: The feature engineering agent analyzes the state of the shared memory data area through a large language model, and after confirming that the data is ready, it infers and decides to call the feature construction tool; S4.2: The feature engineering agent reads the output of the data processing agent from the shared memory data area, aggregates and calculates features by user group, selects appropriate feature templates from the feature template library for calculation, and generates user-level feature vectors. The feature template library contains the following predefined feature calculation functions, divided into four categories: basic activity features include: total number of comments. Defined as user Total number of comments, total number of posts Defined as user Total number of posts, number of active days Defined as user The number of different dates for the comment campaign and the average number of comments per day. Defined as user The average daily number of comments; time pattern characteristics include: median comment time interval. Defined as user Median time interval between adjacent comments, burst rate Defined as ,in Time interval The 95th percentile, The median of the time interval. To determine the frequency and regularity of outbreaks within a short time window. Defined as the standard deviation of the time interval between user comments; content characteristics include: the proportion of duplicate content. Defined as user The proportion of repeated text in comments, average text length Defined as user Average text length of all comments, standard deviation of text length Defined as user Standard deviation of all comment text lengths, lexical diversity Defined as , For a unique set of words, Total vocabulary set, proportion of spam keywords Defined as user The percentage of comments containing spam / promotional keywords and the proportion of most common domain names. Defined as user The proportion of the most common domain names in the URLs of comments out of all valid domain names; behavioral characteristics include: the proportion of comments containing URLs. Defined as user The proportion of comments containing URLs, the proportion of command comments Defined as user The proportion of comments starting with @ or !, and the proportion of short comments. Defined as user The proportion of comments with text length not exceeding the threshold T, and the keyword identifiers for username and bot. Defined as user Does the username contain a binary identifier for the keyword "Bot"? S4.3: The feature engineering agent writes the calculated user-level feature vectors into the feature region of shared memory for downstream agents to read and use; Step S5 specifically includes the following steps: S5.1: The rule-discriminating agent analyzes the state of the shared memory feature region through a large language model. After confirming that the feature data is ready, it infers and decides to call the rule generation tool and the rule application tool in sequence. S5.2: The rule-based discriminative agent reads feature data from the feature region of shared memory. It adopts a rule base based on a preset absolute threshold. Each user behavior characteristic is examined item by item, and each rule is defined as follows:

[0009] in Indicates the feature being detected. It is a comparison operator used to compare feature values ​​and a threshold. For the threshold, The rule template library contains 9 predefined rule templates, with the high-frequency posting rule used to detect the average number of comments per user per day. Whether it exceeds the 95th percentile, and the sudden activity rules are used to detect the explosiveness of user activity. Whether it exceeds the 95th percentile, the content duplication rule is to detect the proportion of duplicate content by users. Whether it exceeds the 95th percentile and the low diversity rule are used to detect the diversity of user vocabulary. Whether it exceeds the 5th percentile, and the mechanical length rule are used to detect the standard deviation of user text length. Whether the number of comments is abnormally low (below the 5th percentile) and the total number of comments is high (above the 75th percentile), and whether the link spamming rule detects the proportion of user comments containing URLs. Whether it exceeds the 95th percentile, the spam keyword rule is to detect the proportion of spam or promotional keywords in user reviews. Whether it exceeds the 90th percentile, the single-domain spamming rule is to detect the proportion of a single domain in the user's URL. Whether it exceeds a fixed threshold, the command robot rules are used to detect the proportion of user-commanded comments. Whether it exceeds a fixed threshold; when a feature meets the triggering condition of the corresponding rule, record the triggering result and sum it according to the rule weight to obtain the rule risk score. ,in As weight, For the rules to be used by users The triggered result, in turn, outputs the user's risk level assessment result based on the score range:

[0010] in This corresponds to a high-risk level. It is classified as a medium-risk level. The risk level is set to low; then the rule detection results are written to the rule area in shared memory. Step S6 specifically includes the following steps: S6.1: The modeling agent analyzes the state of the shared memory feature region through the large language model, and after confirming that the feature data is ready, it infers and decides to call the model training tool; S6.2: Modeling agent reads feature data The features are preprocessed, including feature type selection, missing value handling, and feature standardization.

[0011] in and Features The mean and variance of the values; thus forming standardized feature inputs that can be used for model training and inference; S6.3: Based on a small-sample labeled dataset, the modeling agent first trains an unsupervised anomaly detection model to generate pseudo-labels to expand the training data. Specifically, the Isolation Forest algorithm is used, and the contamination parameter is set to control the proportion of abnormal samples. An anomaly risk score is output for user samples. Abnormal samples return -1, normal samples return 1, and the abnormal marker is mapped to a probability value. Based on the anomaly detection results, pseudo-labels are generated:

[0012] in The pseudo-label threshold is determined by the anomaly score; thus, high-risk anomaly samples are marked as suspected robot samples, and low-risk anomaly samples are marked as normal user samples, for use in subsequent supervised model training or fusion judgment. S6.4: The modeling agent trains multiple supervised classification models based on the pseudo-labels described in step S4.3, specifically including a Gradient Boosting classifier and a Random Forest classifier, and outputs the prediction probability of each model that the user is a robot. Furthermore, feature contribution information for explanation is generated through feature importance analysis; S6.5: After observing the model training results, the modeling agent uses the large language model to infer and decide to call the prediction tool and threshold optimization tool to fuse the outputs of the anomaly detection model and the supervised classification model. A weighted ensemble probability fusion strategy is then used to obtain the comprehensive prediction probability. :

[0013] in The final detection result is determined based on a preset threshold.

[0014] The final classification threshold Calculated using the statistical threshold method , and These are the mean and standard deviation of the fusion probability for all users, respectively. Step S7 specifically includes the following steps: S7.1: Explanation: The generating agent analyzes the state of the shared memory model area and rule area through the large language model, and after confirming that the detection results and rule data are ready, it makes an inference decision to call the visualization analysis tool; S7.2: Explanation: The agent calculates the Z-score for each feature to identify anomalous features.

[0015] in For the user's value on this feature, and These are the mean and standard deviation of the feature in the reference sample, respectively; when That is, when the threshold is exceeded, the feature is marked as an abnormal feature and the feature information of the abnormal feature is recorded; S7.3: The interpreter generates key explanatory information for a single user based on the fused detection results and calls a large language model to generate natural language interpretations. At the same time, it performs statistical summary of all results and generates a final report containing information such as the number of analyzed users, the number of detected robots, the detection rate, and the confidence level. It supports outputting user detection results in JSON, Markdown, or CSV format, thus completing the entire detection process.

[0016] The advantages of this invention are as follows: This invention is aimed at the detection task of social platform robots, and solves the problem that existing detection methods are difficult to achieve collaborative reasoning and dynamic decision-making among multi-dimensional information. It realizes effective detection of social robots, and combines large models for intelligent analysis. It adopts multi-agent collaboration to complete data processing, feature construction, rule discrimination and multi-model fusion detection, reducing the dependence on a single rule or a single model, and greatly improving the accuracy and robustness of detection. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of the method of the present invention.

[0019] Figure 2 This is a structural framework diagram of the multi-agent system of the present invention.

[0020] Figure 3 This is a framework diagram of a specific implementation method of the present invention. Specific implementation methods

[0021] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0022] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Every smaller range between any stated value or intermediate value within a stated range, and any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0023] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0024] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will also be readily apparent to those skilled in the art. This application specification and embodiments are merely exemplary.

[0025] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.

[0026] Unless otherwise specified, the term "parts" in this invention refers to parts by weight.

[0027] This method provides a multi-agent-based social platform bot detection approach. With the widespread application of social networks, the number of users on social platforms has experienced exponential growth. Simultaneously, a large number of social bots driven by automated programs have emerged within these platforms, posing potential risks to the public opinion environment, platform governance, and public safety. Existing detection technologies mostly employ single-model, single-process methods, which are prone to problems such as insufficient generalization, false positives and false negatives, and weak interpretability when facing real-world platform environments where bot strategies iterate rapidly, especially in communities like Reddit where comment structures are central. Therefore, this invention proposes a multi-agent collaborative detection process for social platforms like Reddit, achieving more accurate identification of social bots to support platform governance and manual review. The specific implementation steps include the following: Step S1: Create a social platform API connection instance to collect historical comment data from target users and obtain the raw dataset; S1.1: Create an authorized Reddit API connection instance and call the interface to obtain the target user. Comment history ,in The total number of comments for this user; S1.2: Extract core fields for each comment record: unique comment identifier, post ID, parent comment ID (used to construct comment hierarchy), comment author, comment content, comment creation time, comment score, and sub-forum name. Further obtain detailed information about the related post based on the post ID field, including post title, post content, post author, post creation time, post score, total number of comments, and like ratio. S1.3: Implement rate control and exponential backoff retry mechanisms when crawling data. Traffic control is performed by checking the remaining number of API requests. Let the remaining number of API requests be... The threshold is ,when It automatically sleeps and waits; when a network anomaly is encountered, it retryes exponentially according to the initial waiting time, up to the [number]th [time]. The waiting time for the next retry is:

[0028] in This is the initial waiting time. It is a random perturbation term, with a maximum number of retries. ; Step S2: Build an orchestrator-multi-agent collaborative detection model driven by a large language model; The structure of the orchestrator-multi-agent cooperative detection model is as follows: multi-agent orchestrator, multi-agent cooperative detection module, task input terminal and result output terminal. The multi-agent cooperative detection module includes shared memory and five specialized agents that are communicatively connected to the shared memory. The orchestrator is connected to the multi-agent cooperative detection module and calls the multi-agent agents as needed according to instructions. The five specialized agents include a data processing agent, a feature engineering agent, a rule discrimination agent, a modeling agent, and an interpretation and generation agent; the multi-agent orchestrator performs dynamic planning and scheduling based on a large language model, and is used to sequentially call the five specialized agents and output the social robot detection results; the shared memory is used to realize cross-agent data exchange and state synchronization; Each agent employs a "reasoning-execution-observation" loop mechanism: first, the large language model analyzes the current context to make reasoning decisions; then, the selected tool is executed to complete the specific operation; finally, the execution result is observed and the context state is updated. The shared memory adopts a namespace isolation mechanism, which is divided into five namespaces: data area, feature area, rule area, model area, and interpretation area. It supports key-value storage and historical record tracking, enabling cross-agent data exchange and state synchronization. Step S3: The data processing agent constructs a hierarchical relationship of comments on the input data, and performs cleaning, standardization, and quality checks to form a preprocessed structured dataset. Write to shared memory; S3.1: After the data processing agent reads the data loading results, it constructs the comment hierarchy using the parent comment data. Then, the large language model infers and decides to call the data cleaning tool to perform data cleaning and standardization processing, including deleting completely duplicate rows, removing invalid records where the comment author field is empty or "[deleted]", removing invalid comments where the comment body content field is empty, "[removed]", or "[deleted]", filtering records of deleted users and automatic review robots, cleaning whitespace characters from text fields, converting timestamps to standard datetime format and sorting them by time, and filtering low-activity users with fewer than 5 comments to ensure the effectiveness of feature calculation. S3.2: The data processing agent generates data quality description information and data structure definition, calculates the proportion of missing values ​​and the distribution of unique values ​​in each field, evaluates the length distribution of text fields, and writes the cleaned structured data into the shared data area for downstream agents to read and use. Step S4: The feature engineering agent reads structured data from shared memory and calculates user-level multidimensional behavioral feature vectors. And write it to shared memory; S4.1: The feature engineering agent analyzes the state of the shared memory data area through a large language model, and after confirming that the data is ready, it infers and decides to call the feature construction tool; S4.2: The feature engineering agent reads the output of the data processing agent from the shared memory data area, aggregates and calculates features by user group, selects appropriate feature templates from the feature template library for calculation, and generates user-level feature vectors. The feature template library contains the following predefined feature calculation functions, divided into four categories: basic activity features include: total number of comments. Defined as user Total number of comments, total number of posts Defined as user Total number of posts, number of active days Defined as user The number of different dates for the comment campaign and the average number of comments per day. Defined as user The average daily number of comments; time pattern characteristics include: median comment time interval. Defined as user Median time interval between adjacent comments, burst rate Defined as ,in Time interval The 95th percentile, The median of the time interval. To determine the frequency and regularity of outbreaks within a short time window. Defined as the standard deviation of the time interval between user comments; content characteristics include: the proportion of duplicate content. Defined as user The proportion of repeated text in comments, average text length Defined as user Average text length of all comments, standard deviation of text length Defined as user Standard deviation of all comment text lengths, lexical diversity Defined as , For a unique set of words, Total vocabulary set, proportion of spam keywords Defined as user The percentage of comments containing spam / promotional keywords and the proportion of most common domain names. Defined as user The proportion of the most common domain names in the URLs of comments out of all valid domain names; behavioral characteristics include: the proportion of comments containing URLs. Defined as user The proportion of comments containing URLs, the proportion of command comments Defined as user The proportion of comments starting with @ or !, and the proportion of short comments. Defined as user The proportion of comments with text length not exceeding the threshold T, and the keyword identifiers for username and bot. Defined as user Does the username contain a binary identifier for the keyword "Bot"? S4.3: The feature engineering agent will compute the completed user-level feature vectors. The feature area is written to the shared memory for downstream agents to read and use; Step S5: The rule-determining agent reads the feature data, performs rule determination, judges whether each feature is matched, and outputs the rule risk score and writes it to the shared memory. S5.1: The rule-discriminating agent analyzes the state of the shared memory feature region through a large language model. After confirming that the feature data is ready, it infers and decides to call the rule generation tool and the rule application tool in sequence. S5.2: The rule-based discriminative agent reads feature data from the feature region of shared memory. It adopts a rule base based on a preset absolute threshold. Each user behavior characteristic is examined item by item, and each rule is defined as follows:

[0029] in Indicates the feature being detected. It is a comparison operator used to compare feature values ​​and a threshold. For the threshold, The rule template library contains 9 predefined rule templates, with the high-frequency posting rule used to detect the average number of comments per user per day. Whether it exceeds the 95th percentile, and the sudden activity rules are used to detect the explosiveness of user activity. Whether it exceeds the 95th percentile, the content duplication rule is to detect the proportion of duplicate content by users. Whether it exceeds the 95th percentile and the low diversity rule are used to detect the diversity of user vocabulary. Whether it exceeds the 5th percentile, and the mechanical length rule are used to detect the standard deviation of user text length. Whether the number of comments is abnormally low (below the 5th percentile) and the total number of comments is high (above the 75th percentile), and whether the link spamming rule detects the proportion of user comments containing URLs. Whether it exceeds the 95th percentile, the spam keyword rule is to detect the proportion of spam or promotional keywords in user reviews. Whether it exceeds the 90th percentile, the single-domain spamming rule is to detect the proportion of a single domain in the user's URL. Whether it exceeds a fixed threshold, the command robot rules are used to detect the proportion of user-commanded comments. Whether it exceeds a fixed threshold; when a feature meets the triggering condition of the corresponding rule, record the triggering result and sum it according to the rule weight to obtain the rule risk score. ,in As weight, For the rules to be used by users The triggered result, in turn, outputs the user's risk level assessment result based on the score range:

[0030] in This corresponds to a high-risk level. It is classified as a medium-risk level. The risk level is set to low; then the rule detection results are written to the rule area in shared memory. Step S6: The modeling agent reads feature data, trains anomaly detection and supervised classification models, performs multi-model fusion to obtain robot risk assessment results, and writes them into shared memory; S6.1: The modeling agent analyzes the state of the shared memory feature region through the large language model, and after confirming that the feature data is ready, it infers and decides to call the model training tool; S6.2: Modeling agent reads feature data The features are preprocessed, including feature type selection, missing value handling, and feature standardization.

[0031] in and Features The mean and variance of the values ​​are used to form standardized feature inputs that can be used for model training and inference. S6.3: Based on a small-sample labeled dataset, the modeling agent first trains an unsupervised anomaly detection model to generate pseudo-labels to expand the training data. Specifically, the Isolation Forest algorithm is used, and the contamination parameter is set to control the proportion of abnormal samples. An anomaly risk score is output for user samples. Abnormal samples return -1, normal samples return 1, and the abnormal marker is mapped to a probability value. Based on the anomaly detection results, pseudo-labels are generated:

[0032] in The pseudo-label threshold is determined by the anomaly score; thus, high-risk anomaly samples are marked as suspected robot samples, and low-risk anomaly samples are marked as normal user samples, for use in subsequent supervised model training or fusion judgment. S6.4: The modeling agent trains multiple supervised classification models based on the pseudo-labels described in step S4.3, specifically including a Gradient Boosting classifier and a Random Forest classifier, and outputs the prediction probability of each model that the user is a robot. Furthermore, feature contribution information for explanation is generated through feature importance analysis; S6.5: After observing the model training results, the modeling agent uses the large language model to infer and decide to call the prediction tool and threshold optimization tool to fuse the outputs of the anomaly detection model and the supervised classification model. A weighted ensemble probability fusion strategy is then used to obtain the comprehensive prediction probability. :

[0033] in The final detection result is determined based on a preset threshold.

[0034] The final classification threshold Calculated using the statistical threshold method , and These are the mean and standard deviation of the fusion probability for all users, respectively. Step S7: Interpret and generate the intelligent agent to read the model detection results and rule data, and fuse them to output the final interpretable social robot detection results; S7.1: Explanation: The generating agent analyzes the state of the shared memory model area and rule area through the large language model, and after confirming that the detection results and rule data are ready, it makes an inference decision to call the visualization analysis tool; S7.2: Explanation: The agent calculates the Z-score for each feature to identify anomalous features.

[0035] in For the user's value on this feature, and These are the mean and standard deviation of the feature in the reference sample, respectively; when That is, when the threshold is exceeded, the feature is marked as an abnormal feature and the feature information of the abnormal feature is recorded; S7.3: The interpreter generates key explanatory information for a single user based on the fused detection results and calls a large language model to generate natural language interpretations. At the same time, it performs statistical summary of all results and generates a final report containing information such as the number of analyzed users, the number of detected robots, the detection rate, and the confidence level. It supports outputting user detection results in JSON, Markdown, or CSV format, thus completing the entire detection process.

[0036] The following is a specific example, using user data collected on the Reddit platform as an example, to perform detection and analysis on the user "Canna_Tips": Step S1: Collect the user's comment data via the Reddit API, obtaining a total of 2000 comment records. The data processing agent preprocesses the data. Step S2: The user-level feature vector calculated by the feature engineering agent includes: total number of comments. Items, number of comments per day Items / day, median time interval Day (approximately 0.007 seconds), burst speed The proportion of duplicate content (50.25%), vocabulary diversity (0.54%), proportion of spam keywords (100%) URL ratio (100%) Percentage of the most common domain names (100%) Step S3: The predefined core detection rules and their thresholds and weights are as follows: Ultra-high frequency comment rule: determines if the number of daily comments is greater than 50, with a weight of 1.2; Extreme burst rule: determines if the burst rate is greater than 100, with a weight of 1.5; Extremely high repetition rate rule: determines if the repetition ratio is greater than 0.8, with a weight of 1.3; Extremely low diversity rule: determines if the lexical diversity is less than 0.3, with a weight of 1.1; Ultra-short time interval rule: determines if the median time interval is less than 60 seconds, with a weight of 1.4; Mechanical text length rule: determines if the standard deviation of the text length is less than 5, with a weight of 1.0; Large number of links rule: determines the URL ratio. Whether the ratio of command judgment commands is greater than 0.5 and has a weight of 1.4, and whether the ratio of command judgment commands is greater than 0.6 and has a weight of 1.6; the rule judgment agent detects the user's application rule system, and the detection results are as follows: ultra-high frequency comment rule trigger (daily comments 2000 greater than threshold 50, weight 1.2), extreme sudden rule trigger (explosiveness 100.8 greater than threshold 100, weight 1.5), extremely low diversity rule trigger (vocabulary diversity 0.54% less than threshold 30%, weight 1.1), large number of link rule trigger (URL ratio 100% greater than threshold 50%, weight 1.4); rule risk score. ,because It was classified as a high-risk level; Step S4: The modeling agent uses three models for prediction, with Isolation Forest outputting probabilities. Gradient Boosting output probability Random Forest output probability Calculated according to the weighted fusion strategy The final overall probability was 0.86, which is greater than the classification threshold, and it was determined to be a robot. Step S5: Explain the detection report generated by the intelligent agent, which shows that the user is a high-risk bot with a confidence level of 86%. Key evidence includes: abnormal comment frequency (2000 comments per day), extremely high proportion of spam keywords, extremely low vocabulary diversity, and hitting 4 high-weight rules.

[0037] By implementing the multi-agent-based social platform robot detection method proposed in this invention, the shortcomings of previous detection methods—which typically rely on single models or single-process discrimination and struggle to perform collaborative reasoning and dynamic decision-making based on multi-dimensional information such as text semantic analysis and behavioral pattern modeling—are overcome. This invention can generate user-level multi-dimensional behavioral features and rule discrimination based on the construction of comment hierarchy relationships and structured data, and obtain robot judgment results through multi-model fusion, thereby outputting interpretable and visually interpretable analysis results, meeting the requirements of practical use. Furthermore, this method also provides a reference detection scheme for other similar platforms.

[0038] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for detecting social platform robots based on multi-agent intelligence, characterized in that, Includes the following steps: S1: Create a social platform API connection instance to collect historical comment data from target users and obtain the raw dataset; S2: Construct an orchestrator-multi-agent collaborative detection model driven by a large language model; the structure of the orchestrator-multi-agent collaborative detection model is as follows: the multi-agent orchestrator is connected to the multi-agent collaborative detection module, the multi-agent collaborative detection module includes shared memory and five specialized agents that are communicatively connected to the shared memory, the orchestrator is connected to the multi-agent collaborative detection module, and calls the multi-agent as needed according to instructions; The five specialized intelligent agents include a data processing intelligent agent, a feature engineering intelligent agent, a rule discrimination intelligent agent, a modeling intelligent agent, and an interpretation and generation intelligent agent; The multi-agent orchestrator performs dynamic planning and scheduling based on a large language model, and is used to sequentially call the five specialized agents and output the social robot detection results; The shared memory is used to enable data exchange and state synchronization across agents; S3: The data processing agent constructs a hierarchical relationship of comments on the input data, and performs cleaning, standardization, and quality checks to form a preprocessed structured dataset. Write to shared memory; S4: The feature engineering agent reads structured data from shared memory and calculates user-level multidimensional behavioral feature vectors. And write it to shared memory; S5: The rule-determining agent reads feature data, performs rule determination, judges whether each feature is matched, and outputs the rule risk score and writes it to shared memory; S6: The modeling agent reads feature data, trains anomaly detection and supervised classification models, performs multi-model fusion to obtain robot risk assessment results and writes them into shared memory; S7: Interpret and generate intelligent agents that read model detection results and rule data, and fuse them to output the final interpretable social robot detection results.

2. The method for detecting social platform robots based on multi-agent technology according to claim 1, characterized in that, Step S1 includes: S1.1: Create an authorized social platform API connection instance and call the interface to obtain target users. Comment history ,in The total number of comments for this user; S1.2: Extract core fields for each comment record, including the comment's unique identifier, the ID of the post to which it belongs, the parent comment, the comment author, the comment text, the creation time, the comment score, and the sub-forum name, and further obtain detailed post information based on the ID of the post to which it belongs; S1.3: Implement rate control and exponential backoff retry mechanisms during data crawling. Traffic control is performed by checking the remaining number of API requests. Let the remaining number of API requests be... The threshold is ,when It automatically sleeps and waits; when a network anomaly is encountered, it retryes exponentially according to the initial waiting time, up to the [number]th [time]. The waiting time for the next retry is: in This is the initial waiting time. It is a random disturbance term, and the maximum number of retries is .

3. The method for detecting social platform robots based on multi-agent technology according to claim 2, characterized in that, The orchestrator-multi-agent collaborative detection model further includes: the data processing agent, feature engineering agent, rule discrimination agent, modeling agent, and interpretation generation agent all adopt a "reasoning-execution-observation" loop mechanism: first, the large language model analyzes the current context to make reasoning decisions, then the selected tool is executed to complete the specific operation, and finally the execution result is observed and the context state is updated; the shared memory adopts a namespace isolation mechanism, divided into five namespaces: data area, feature area, rule area, model area, and interpretation area, supporting key-value storage and historical record tracking, and realizing cross-agent data exchange and state synchronization.

4. The method for detecting social platform robots based on multi-agent technology according to claim 3, characterized in that, Step S3 includes: S3.1: After the data processing agent reads the data loading results, it uses the parent comment data to construct the comment hierarchy. Then, the large language model infers and decides to call the data cleaning tool to perform data cleaning and standardization processing, including deleting completely duplicate rows, removing records with missing necessary fields, filtering deleted user and automatic review robot records, cleaning whitespace characters from text fields, converting timestamps to standard datetime format and sorting them by time, and filtering low-activity users with fewer than 5 comments to ensure the effectiveness of feature calculation. S3.2: The data processing agent generates data quality description information and data structure definition, and processes the cleaned structured data. Write it to the shared data area for downstream intelligent agents to read and use.

5. The method for detecting social platform robots based on multi-agent technology according to claim 4, characterized in that, Step S4 includes: S4.1: The feature engineering agent analyzes the state of the shared memory data area through a large language model, and after confirming that the data is ready, it infers and decides to call the feature construction tool; S4.2: The feature engineering agent reads the output of the data processing agent from the shared memory data area, aggregates and calculates features by user group, selects appropriate feature templates from the feature template library for calculation, and generates user-level feature vectors. The feature template library contains the following predefined feature calculation functions, divided into four categories: basic activity features include: total number of comments. Defined as user Total number of comments, total number of posts Defined as user Total number of posts, number of active days Defined as user The number of different dates for the comment campaign and the average number of comments per day. Defined as user The average daily number of comments; time pattern characteristics include: median comment time interval. Defined as user Median time interval between adjacent comments, burst rate Defined as ,in Time interval The 95th percentile, The median of the time interval. To determine the frequency and regularity of outbreaks within a short time window. Defined as the standard deviation of the time interval between user comments; content characteristics include: the proportion of duplicate content. Defined as user The proportion of repeated text in comments, average text length Defined as user Average text length of all comments, standard deviation of text length Defined as user Standard deviation of all comment text lengths, lexical diversity Defined as , For a unique set of words, Total vocabulary set, proportion of spam keywords Defined as user The percentage of comments containing spam / promotional keywords and the proportion of most common domain names. Defined as user The proportion of the most common domain names in the URLs of comments out of all valid domain names; behavioral characteristics include: the proportion of comments containing URLs. Defined as user The proportion of comments containing URLs, the proportion of command comments Defined as user The proportion of comments starting with @ or !, and the proportion of short comments. Defined as user The proportion of comments whose text length does not exceed the threshold T, and the keyword identifiers for username and bot. Defined as user Does the username contain a binary identifier for the keyword "Bot"? S4.3: The feature engineering agent writes the calculated user-level feature vectors into the feature region of shared memory for downstream agents to read and use.

6. The method for detecting social platform robots based on multi-agent technology according to claim 5, characterized in that, Step S5 includes: S5.1: The rule-discriminating agent analyzes the state of the shared memory feature region through a large language model. After confirming that the feature data is ready, it infers and decides to call the rule generation tool and the rule application tool in sequence. S5.2: The rule-based discriminative agent reads feature data from the feature region of shared memory. It adopts a rule base based on a preset absolute threshold. Each user behavior characteristic is examined item by item, and each rule is defined as follows: in Indicates the feature being detected. It is a comparison operator used to compare feature values ​​and threshold values. For the threshold, The rule template library contains 9 predefined rule templates, with the high-frequency posting rule used to detect the average number of comments per user per day. Whether it exceeds the 95th percentile, and the sudden activity rules are used to detect the explosiveness of user activity. Whether it exceeds the 95th percentile, the content duplication rule is to detect the proportion of duplicate content by users. Whether it exceeds the 95th percentile and the low diversity rule are used to detect the diversity of user vocabulary. Whether it exceeds the 5th percentile, and the mechanical length rule are used to detect the standard deviation of user text length. Whether the number of comments is abnormally low (below the 5th percentile) and the total number of comments is high (above the 75th percentile), and whether the link spamming rule detects the proportion of user comments containing URLs. Whether it exceeds the 95th percentile, the spam keyword rule is to detect the proportion of spam or promotional keywords in user reviews. Whether it exceeds the 90th percentile, the single-domain spamming rule is to detect the proportion of a single domain in the user's URL. Whether it exceeds a fixed threshold, the command robot rules are used to detect the proportion of user-commanded comments. Whether it exceeds a fixed threshold; when a feature meets the triggering condition of the corresponding rule, record the triggering result and sum it according to the rule weight to obtain the rule risk score. ,in As weight, For the rules to be used by users The triggered result, in turn, outputs the user's risk level assessment result based on the score range: in This corresponds to a high-risk level. It is classified as a medium-risk level. The risk level is set to low; then the rule detection results are written to the rule area in shared memory.

7. The method for detecting social platform robots based on multi-agent technology according to claim 6, characterized in that, Step S6 includes: S6.1: The modeling agent analyzes the state of the shared memory feature region through the large language model, and after confirming that the feature data is ready, it infers and decides to call the model training tool; S6.2: Modeling agent reads feature data The features are preprocessed, including feature type selection, missing value handling, and feature standardization. in and Features The mean and variance of the values; thus forming standardized feature inputs that can be used for model training and inference; S6.3: Based on a small-sample labeled dataset, the modeling agent first trains an unsupervised anomaly detection model to generate pseudo-labels to expand the training data. Specifically, the Isolation Forest algorithm is used, and the contamination parameter is set to control the proportion of abnormal samples. An anomaly risk score is output for user samples. Abnormal samples return -1, normal samples return 1, and the abnormal marker is mapped to a probability value. Based on the anomaly detection results, pseudo-labels are generated: in The pseudo-label threshold is determined by the anomaly score; thus, high-risk anomaly samples are marked as suspected robot samples, and low-risk anomaly samples are marked as normal user samples, for use in subsequent supervised model training or fusion judgment. S6.4: The modeling agent trains multiple supervised classification models based on the pseudo-labels described in step S4.3, specifically including a Gradient Boosting classifier and a Random Forest classifier, and outputs the prediction probability of each model that the user is a robot. Furthermore, feature contribution information for explanation is generated through feature importance analysis; S6.5: After observing the model training results, the modeling agent uses the large language model to infer and decide to call the prediction tool and threshold optimization tool to fuse the outputs of the anomaly detection model and the supervised classification model. A weighted ensemble probability fusion strategy is then used to obtain the comprehensive prediction probability. : in The final detection result is determined based on a preset threshold. The final classification threshold Calculated using the statistical threshold method , and These are the mean and standard deviation of the fusion probability for all users, respectively.

8. The method for detecting social platform robots based on multi-agent technology according to claim 7, characterized in that, Step S7 includes: S7.1: Explanation: The generating agent analyzes the state of the shared memory model area and rule area through the large language model, and after confirming that the detection results and rule data are ready, it makes an inference decision to call the visualization analysis tool; S7.2: Explanation: The agent calculates the Z-score for each feature to identify anomalous features. in For the user's value on this feature, and These are the mean and standard deviation of the feature in the reference sample, respectively; when That is, when the threshold is exceeded, the feature is marked as an abnormal feature and the feature information of the abnormal feature is recorded; S7.3: The interpreter generates key explanatory information for a single user based on the fused detection results and calls a large language model to generate natural language interpretations. At the same time, it performs statistical summary of all results and generates a final report containing information such as the number of analyzed users, the number of detected robots, the detection rate, and the confidence level. It supports outputting user detection results in JSON, Markdown, or CSV format, thus completing the entire detection process.

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