Activeness state judgment device and method considering multi-dimensional characteristics of social network account

Abstract

The invention discloses a social network account state judgment method. The method comprises the following steps: S1, obtaining state data of a target account; s2, extracting account basic information, account fan information and account speaking information from the state data; and S3, comparing the account basic information, the account fan information and the account speaking information with preset conditions, and determining the state of the target account according to a comparison result. The invention further discloses a judgment device. According to the invention, the state of the social network account can be accurately judged.

Description

Technical Field

[0001] The present invention relates to the technical field of computer information, and more specifically, to a method and device for determining the status of a social network account. Background Art

[0002] With the widespread use of social networks, the performance of accounts on social platforms has a significant impact on information dissemination, social interaction, and market dynamics. The status changes of social network accounts not only reflect the activity level of the account, but also serve as an important basis for judging its influence and development trend. Therefore, how to effectively track these status changes has become a key technical issue. Existing social network account status tracking technologies mainly focus on data display and trend analysis, usually with visualization and reports as the final output. However, these technologies have deficiencies in real-time status tracking, automation, and anomaly detection, especially when dealing with rapidly changing social environments, they often lack flexibility and accuracy.

[0003] Therefore, it is necessary to design a technical solution that can overcome the above-mentioned defects. Summary of the invention

[0004] An object of the present invention is to provide a method and device for determining the status of a social network account, which can more accurately determine the status of a social network account.

[0005] In order to achieve these purposes and other advantages of the present invention, according to one aspect of the present invention, the present invention provides a method for determining the status of a social network account, including: S1: obtaining status data of a target account; S2: extracting basic account information, account fan information and account speech information from the status data; S3: comparing the basic account information, the account fan information and the account speech information with preset conditions, and determining the status of the target account according to the comparison result.

[0006] Furthermore, the account fan information includes at least the fan increase / decrease rate and fan type distribution, and the account speech information includes at least speech frequency, speech time period, speech content, interaction frequency, and interaction content.

[0007] Furthermore, the preset condition is determined based on historical data of the account basic information, the account fan information and the account speech information.

[0008] Furthermore, the preset conditions are used to establish status judgment rules in advance, and the status judgment rules include a corresponding relationship between the preset conditions and the account status; in S3, the status of the target account is determined according to the comparison result and the status judgment rules.

[0009] Furthermore, it also includes: S4: if the target account is in a normal state, extracting the interactive accounts and the corresponding account types, interaction types, interaction times and interaction contents of multiple predetermined time periods; S5: using the target account and the interactive account to establish a isomorphic graph, and calculating the interaction index according to the account type, the interaction type, the interaction times and the interaction content; S6: constructing an interaction change curve using the interaction indexes of multiple predetermined time periods; S7: judging whether the target account has potential abnormal risks according to the interaction change curve.

[0010] According to another aspect of the present invention, a device for determining the status of a social network account is also provided, including: an acquisition module for acquiring status data of a target account; a first extraction module for extracting basic account information, account fan information and account speech information from the status data; a determination module for comparing the basic account information, the account fan information and the account speech information with preset conditions, and determining the status of the target account according to the comparison result.

[0011] Furthermore, the account fan information includes at least the fan increase / decrease rate and fan type distribution, and the account speech information includes at least speech frequency, speech time period, speech content, interaction frequency, and interaction content.

[0012] Furthermore, the preset condition is determined based on historical data of the account basic information, the account fan information and the account speech information.

[0013] Furthermore, the preset conditions are used to establish status judgment rules in advance, and the status judgment rules include a corresponding relationship between the preset conditions and the account status; and the status of the target account is determined according to the comparison result and the status judgment rules.

[0014] Furthermore, it also includes: a second extraction module, which is used to extract the interactive accounts and the corresponding account types, interaction types, interaction times and interaction contents of multiple predetermined time periods if the target account is in a normal state; a calculation module, which is used to establish a isomorphic graph using the target account and the interactive account, and calculate the interaction index according to the account type, the interaction type, the interaction times and the interaction content; a construction module, which constructs an interaction change curve using the interaction indexes of multiple predetermined time periods; and a judgment module, which is used to judge whether the target account has potential abnormal risks based on the interaction change curve.

[0015] The present invention has at least the following beneficial effects: The present invention obtains the status data of the target account, extracts the basic information of the account, the fan information of the account and the speech information of the account from the status data, compares the basic information of the account, the fan information of the account and the speech information of the account with preset conditions, and determines the status of the target account according to the comparison result; the present invention can accurately judge the status of the social network account through a comprehensive analysis of the account behavior and platform feedback, clearly distinguish between normal, permanently inactive, semi-active and other states, reduce misjudgment, and update the results in time when the state changes, so as to ensure the timeliness and accuracy of the account status judgment; the present invention adopts an automated data collection and analysis method, reduces the need for manual intervention, improves the efficiency and accuracy of status tracking, and is particularly suitable for batch tracking of large-scale accounts.

[0016] Other advantages, objectives and features of the present invention will be embodied in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a framework diagram of an embodiment of the present application; Figure 2 A flowchart of an embodiment of the present application; Figure 3 A flowchart of state analysis for an embodiment of the present application; Figure 4 This is a sample diagram of an embodiment of the present application. DETAILED DESCRIPTION

[0018] The present invention is further described in detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0019] It should be understood that the terms such as "having", "including" and "comprising" used in the embodiments of the present application do not exclude the existence or addition of one or more other elements or their combinations. All directional indications (such as up, down, left, right, front, back...) in the embodiments of the present application are only used to explain the relative position relationship, movement, etc. between the components in a certain specific posture. If the specific posture changes, the directional indication will also change accordingly. When an element is referred to as "fixed on" or "set on" another element, it can be directly on the other element or there may be a centering element at the same time. When an element is referred to as "connecting" another element, it can be directly connected to another element or it can be indirectly connected to another element through a centering element. The description of "first", "second", etc. in the embodiments of the present application is only for descriptive purposes, and cannot be understood as indicating or implying its relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" can explicitly or implicitly include at least one of the features.

[0020] It should be noted that the technical solutions between the various embodiments of the present application can be combined with each other, but it must be based on the fact that ordinary technicians in the field can implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0021] The embodiment of the present application provides a method for determining the status of a social network account, including: S1: Get the status data of the target account; Exemplarily, the public data of the account, including homepage information, fan data, published articles, etc., is obtained from the specified link through crawler technology or API interface. The collected raw data will be temporarily stored in memory or database for subsequent analysis and processing; S2: extracting basic account information, account fan information and account speech information from the status data; Exemplarily, the status data obtained in S1 is parsed, such as homepage data capture: basic information such as account name, profile, authentication information, recent activity time, etc. is obtained through the account homepage link; fan and friend attention data capture: the account's interaction quantity, growth trend, interaction behavior and other information are obtained through the page link; article data capture: the content published by the account is analyzed through the article link, including article content, publishing time, reading volume, interaction volume, etc.; S3: Compare the basic account information, the account fan information and the account speech information with preset conditions, and determine the status of the target account according to the comparison result; For example, for the basic information of an account, it is possible to check whether there are any abnormal changes. For example, frequent modification of personal information may be one of the manifestations that the account is controlled by others. For the account fan information, attention is paid to whether the fan growth rate is natural and whether there are a large number of zombie fans. For the account speech information, the quality, frequency and user feedback of the published content are evaluated. Based on the comparative analysis between the above information and the preset conditions, a comprehensive evaluation of the current status of the target account can be obtained. The preset conditions can be set according to the status to be determined. If it is found that the number of fans of an account increases sharply in a short period of time but the interaction rate is extremely low, there may be a situation of buying fake fans. Or if the account suddenly starts to frequently publish advertising content, it may mean that the account is trying to carry out commercial promotion activities. Exemplarily, a decision tree model is used to split layer by layer through fan growth, interaction frequency, publishing frequency, etc., and finally output the account status; each split node represents a decision condition (the most representative one is to directly return the result of the account's basic information. If the account's basic information is displayed as normal, it will continue to determine whether other activities are normal, otherwise it will directly return the abnormal status of the account's basic information), and the final leaf node outputs the status result, assigning a status label to the target account, such as normal, permanently inactive, semi-active, etc.

[0022] In summary, this embodiment obtains the status data of the target account, extracts multi-dimensional features such as basic account information, account fan information, and account speech information from the status data, and compares them with preset conditions to determine the status of the target account according to the comparison result; this embodiment can accurately judge the status of the social network account through a comprehensive analysis of account behavior and platform feedback, clearly distinguish between normal, permanently inactive, semi-active and other states, reduce misjudgment, and update the results in time when the state changes to ensure the timeliness and accuracy of account status judgment; this embodiment can adopt automated data collection and analysis methods, reduce the need for manual intervention, and improve the efficiency and accuracy of status tracking, which is particularly suitable for batch tracking of large-scale accounts; further, this embodiment can also have good scalability, can adapt to the rules and data formats of multiple social platforms, such as: Weibo, Facebook, Twitter, etc., flexibly adjust the status judgment criteria to ensure wide applicability.

[0023] In another embodiment, the account fan information includes at least the fan increase / decrease rate and fan type distribution, and the account speech information includes at least speech frequency, speech time period, speech content, interaction frequency, and interaction content; For example, the fan increase / decrease rate reflects the change in the number of fans of the account in a specific period of time. By recording and analyzing the increase or decrease in the number of fans in different time spans (such as daily, weekly, monthly, etc.) in detail, the corresponding increase / decrease rate can be calculated. Detailed fan increase / decrease rate data can provide a valuable reference for in-depth understanding of the changes in the attractiveness of the account in social networks, the dynamic evolution of the degree of attention, etc. For example, the fan type distribution is classified according to the fan activity, the fan region, the fan age level, the fan gender, the fan occupation background, and the fan interests. For example, different time periods can be set for speech frequency to conduct statistics, such as taking one day as a unit to count how many pieces of content the account has posted in that day; or taking one week or one month as a unit to observe the average number of speeches in a longer period of time; the speech time period focuses on the specific time distribution pattern of the account's speech releases, and by recording the exact time of each speech, it is analyzed in which time period of the day (such as early morning, morning, noon, afternoon, evening, late night, etc.) the account speaks most frequently, or whether there are specific time nodes in a longer time period such as a week or a month, which makes the account more inclined to make speeches at these times; the content of the speech can be developed from multiple angles, such as the field of content, which type of topics such as daily trivia, professional knowledge, entertainment news, social hot spots, etc. dominate; the style of the content, whether it is serious, humorous, concise and clear or lengthy and obscure; the quality of the content, including the length of the content, whether it has depth, accuracy, originality, etc.

[0024] Exemplarily, the forms of interaction include but are not limited to likes, comments, and forwarding. The interaction frequency is comprehensively determined by counting the number of various interactive operations performed by an account on content posted by other users within a certain period of time (such as one day, one week, one month, etc.). A higher interaction frequency means that the account has strong sociality and participation in the social network and can actively communicate and interact with other users.

[0025] For example, the interactive content involves specific text, pictures, videos and other information, including the amount of information, information field, whether it is an advertisement, whether it is meaningless, whether there is substantive content, etc.

[0026] In another embodiment, the preset condition is determined based on the historical data of the basic information of the account, the fan information of the account, and the speech information of the account; Exemplarily, for basic account information, it can be modification frequency, whether it is banned, etc.; for fan information, it can be a healthy fan growth rate range, an ideal fan group structure, etc.; in terms of account speech information, it can be a speech frequency threshold, speech time period range, speech content field or quality, interaction frequency threshold, interaction content field or quality, etc.; all can be determined through statistical historical data, and thresholds can be set to measure changes in account basic information, account fan information and account speech information.

[0027] In another embodiment, the preset condition is used to establish a status judgment rule in advance, and the status judgment rule includes a corresponding relationship between the preset condition and the account status; in S3, the status of the target account is determined according to the comparison result and the status judgment rule; Exemplarily, F1, F2…Fn, Fn represents the input feature value, such as speaking frequency, fan growth rate, interaction quality, etc.; S represents the final account status, and the status is represented by a status code, such as S∈{100,101,…,901}S \in \{100,101, \dots, 901\}S∈{100,101,…,901}; T represents the decision tree model.

[0028] Then the state judgment rule can be expressed as: S=T(F1,F2,…,Fn) The logical structure is: 1. Each node Ni represents a characteristic condition, for example: N1: Is the frequency of speaking abnormal? N2: Is the number of followers abnormal? N3: Is the quality of interaction below the threshold? 2. The decision path is judged by conditions and finally reaches the leaf node to output the account status S.

[0029] 3. Assuming that three features F1 (speech frequency), F2 (fan growth) and F3 (interaction quality) are used, the status judgment rule is: .

[0030] In another embodiment, it also includes: S4: If the target account is in a normal state, extracting the interactive accounts and corresponding account types, interaction types, interaction times and interaction contents in multiple predetermined time periods; This embodiment performs in-depth analysis on the target account in a normal state to mine its potential changes; Exemplarily, the interactive accounts are all other accounts that have interacted with the target account. These interactions can be in any form, such as likes, comments, and reposts; Exemplarily, account types include personal users, corporate accounts, media accounts, and corresponding account fields; Exemplarily, the interaction type is the specific form of each interaction, such as liking, replying to comments, participating in discussions, or sharing content; For example, the number of interactions is the number of interactions between each interactive account and the target account, which can reflect the closeness of the connection between the two; Exemplarily, the interactive content refers to the content involved in the interaction between two parties, including text, pictures, videos, etc.

[0031] S5: establishing a homogeneous graph using the target account and the interactive account, and calculating an interaction index according to the account type, the interaction type, the number of interactions, and the interactive content; For example, a network graph is constructed by taking the target account and all its interactive accounts as nodes and the interactive relationships between them as edges. This graph structure can help to intuitively display the interaction pattern between accounts; For example, based on the above collected information (account type, interaction type, number of interactions, and interaction content), an algorithm is designed to quantify the importance or influence of each interactive account on the target account; this index includes the weight of the account type: different types of accounts may have different degrees of influence on the target account; the weight of the interaction type: some ways of interaction (such as in-depth conversations) may be more meaningful than simple likes; the frequency of interaction: frequent interactions usually mean stronger relationships; the quality of the interactive content: high-quality or constructive interactions should receive higher scores; For example, the interaction index , a i , b i 、c i and d i The account type score, interaction type score, interaction number score and interaction content score corresponding to the ith interactive account are respectively assigned different scores according to preset rules. The account type score is determined according to different subjects and different fields of the interactive account. The interaction type score is determined according to the types of likes and replies. The interaction number score is determined according to the number of interactions. The interaction content score can be determined by the quality of interaction. The quality of interaction can be determined according to the amount of information in the interaction, the number of likes obtained by the interactive content, or the number of keywords.

[0032] S6: constructing an interaction change curve using the interaction indexes of multiple predetermined time periods; Exemplarily, multiple predetermined time periods are selected, such as weekly, monthly or quarterly, and the interaction indexes in the time periods are calculated respectively. The interaction indexes of these time periods are connected into a line to form a trend graph that changes over time; this curve can intuitively show the changing trend of the relationship strength between the target account and its interactors; S7: Determine whether the target account has potential abnormal risk according to the interaction change curve.

[0033] Exemplarily, by observing the overall trend of the interaction change curve, it is determined whether the target account is in a stable state, if the curve shows an upward or downward trend; Detecting the interaction change curve: using machine learning or mathematical analysis technology to analyze the characteristics of the interaction change curve; Exemplarily, the interactive change curves of the normal state and the abnormal state are used to train the convolutional neural network model, the interactive change curves of the normal state and the abnormal state are respectively assigned labels 1 and 0, the interactive change curve of the current time period is input into the neural network model, and the probability of the abnormal state is output.

[0034] The embodiment of the present application also provides a social network account status determination device, comprising: an acquisition module, used to acquire status data of a target account; a first extraction module, used to extract basic account information, account fan information, and account speech information from the status data; a determination module, used to compare the basic account information, the account fan information, and the account speech information with preset conditions, and determine the status of the target account according to the comparison result; This embodiment uses a computer program to construct an acquisition module, a first extraction module, and a determination module. The acquisition module acquires the status data of the target account. The extraction module extracts multi-dimensional features such as basic account information, account fan information, and account speech information from the status data. The determination module compares the basic account information, account fan information, and account speech information with preset conditions, and determines the status of the target account according to the comparison result. This embodiment can accurately judge the status of the social network account through a comprehensive analysis of account behavior and platform feedback, clearly distinguish between normal, permanently inactive, semi-active and other states, reduce misjudgment, and update the results in time when the state changes, ensuring the timeliness and accuracy of account status judgment. This embodiment can adopt an automated data collection and analysis method, reduce the need for manual intervention, and improve the efficiency and accuracy of status tracking, especially suitable for batch tracking of large-scale accounts. Further, this embodiment can also have good scalability, can adapt to the rules and data formats of multiple social platforms, such as: Weibo, Facebook, Twitter, etc., flexibly adjust the status judgment standard, and ensure wide applicability.

[0035] In another embodiment, the account fan information includes at least the fan increase / decrease rate and fan type distribution, and the account speech information includes at least speech frequency, speech time period, speech content, interaction frequency, and interaction content; Exemplarily, the rate of increase or decrease in the number of fans reflects the change in the number of fans of the account within a specific time period; the distribution of fan types, for example, is classified according to the activity of fans, divided according to the geographical origin of fans, distinguished according to the age level of fans, classified according to the gender attributes of fans, classified according to the professional background of fans, and classified according to interests and hobbies; for the frequency of speech, different time periods can be set for statistics, such as taking one day as a unit to count how many pieces of content the account has posted in that day; the speech time period focuses on the specific time distribution pattern of the account's posting of remarks, and by recording the exact time of each speech, it is analyzed in which time period of the day the account is in; the content of the speech can be expanded from multiple angles, such as the theme of the content, the style of the content, the quality of the content, etc.; the forms of interaction include but are not limited to likes, comments, reposts and other operations, and the frequency of interaction is comprehensively determined by counting the number of various interactive operations performed by the account on the content posted by other users within a certain period of time (such as one day, one week, one month, etc.), and the number of interactive operations performed by other users on the content posted by the account; the specific text, pictures, videos and other information involved in the interactive content, including the amount of information, information field, whether it is an advertisement, whether it is meaningless, whether there is substantive content, etc.

[0036] In another embodiment, the preset conditions are determined based on historical data of the account basic information, the account fan information and the account speech information; illustratively, for the account basic information, it may be modification frequency, whether it is banned, etc.; regarding fan information, it may be a healthy fan growth rate range, an ideal fan group structure, etc.; in terms of account speech information, it may be a speech frequency threshold, a speech time period range, a speech content field or quality, an interaction frequency threshold, an interaction content field or quality, etc.

[0037] In another embodiment, the preset conditions are used to establish status judgment rules in advance, and the status judgment rules include the corresponding relationship between the preset conditions and the account status; the status of the target account is determined according to the comparison result and the status judgment rules; illustratively, the account basic information, account fan information and account speech information are compared with the corresponding preset conditions in turn, and then combined with the pre-established status judgment rules to determine the status of the target account.

[0038] In another embodiment, it also includes: a second extraction module, which is used to extract the interactive accounts and the corresponding account types, interaction types, interaction times and interaction contents of multiple predetermined time periods if the target account is in a normal state; a calculation module, which is used to establish a isomorphic graph using the target account and the interactive account, and calculate the interaction index according to the account type, the interaction type, the interaction times and the interaction content; a construction module, which constructs an interaction change curve using the interaction indexes of multiple predetermined time periods; and a judgment module, which is used to judge whether the target account has potential abnormal risks according to the interaction change curve.

[0039] This embodiment uses a computer program to construct a second extraction module, a calculation module, a construction module and a judgment module, conducts in-depth analysis on the target account in a normal state, explores its potential changes, and uses the target account and all its interactive accounts as nodes and the interactive relationships between them as edges to construct a network diagram. Based on the above collected information (account type, interaction type, number of interactions and interaction content), the interaction index is calculated using the interaction index calculation formula, and multiple predetermined time periods are selected to connect the interaction indexes of these time periods into a line. By observing the overall trend, fluctuation, and change characteristics of the interaction change curve, the probability of the existence of potential anomalies is judged.

[0040] In another embodiment, it is specifically implemented by the following devices and methods: a data processing device 101; a data cleaning device 102; a status analysis device 103; a decision device 104; and a continuous updating device 105. Device 101 is used to format the initial data; device 102 uniformly formats various acquired data, such as basic account information, speech information, fan list, and follow-up list; device 103 is used to conduct a comprehensive analysis of the account status of the various acquired information; device 104 is used to make a comprehensive judgment on the results of the analysis and decide on the current status of the account; device 105 is used to continuously track and analyze the current account until the account task is completed.

[0041] Step s21 (data input): Collect the basic information of the target account, the account address link provided by the user or the system, which is the starting point for subsequent analysis. This link can be the URL of the account homepage on the social platform. The system obtains the public data of the account from the specified link through crawler technology or API interface, including homepage information, fan data, published articles, etc. The collected raw data will be temporarily stored in the memory or database for subsequent analysis and processing.

[0042] Step s22 (data link analysis): parse the input account link, capture multi-dimensional detailed information, and build a comprehensive data portrait of the account: Home page data capture: obtain basic information through the account home page link, such as account name, profile, authentication information, recent activity time, etc. Fans and friends follow data capture: obtain account interaction quantity, growth trend, interactive behavior and other information through page links. Article data capture: analyze the content published by the account through article links, including article content, publishing time, reading volume, interaction volume, etc.

[0043] Step s23 (comprehensive judgment of status): Based on the collected and analyzed data, make a comprehensive judgment on the account status: split layer by layer through key features in the data (such as fan growth, interaction frequency, publishing frequency, etc.), and finally output the account status. Each split node represents a decision condition (the most representative one is the direct return result of the basic information of the account. If the basic information of the account is displayed as normal, continue to judge whether other activities are normal, otherwise directly return the abnormal status of the basic information of the account), and the final leaf node outputs the status result.

[0044] Specifically include: Step s31 (feature extraction and aggregation): feature extraction from the data obtained from the data processing module, including the account's behavioral characteristics, interaction trends, fan dynamics, etc.; Step s32 (algorithm analysis and reasoning): use rule-based reasoning and time series analysis algorithms to further analyze the account data; Step s33 (abnormal state identification): the system will pay special attention to abnormal behavior patterns, such as a sudden large-scale decrease in interactions, a sharp change in the number of fans, abnormal content publishing frequency, etc. These abnormal behaviors may indicate that the account has entered an abnormal state, such as highly active, semi-active, sudden active, etc.; Step s34 (comprehensive result judgment): reference Figure 4 The system considers the results of each analysis step and outputs the final account status judgment. If multiple model results are consistent at the same time, the status with a higher priority (such as highly active) will be determined as the final result. According to the priority sorting algorithm, the status information obtained from the basic information of the account has the highest priority and can be directly seen on social media websites. Other fan and speech information has a secondary weight. Figure 4 Various abnormal information in the data is marked as information that may reduce activity.

[0045] Step s35 (result output and feedback): the final status result will be transmitted to the decision module for subsequent processing (such as triggering an alarm, updating the database); Step s24 (data format standardization): Convert the analysis results into a unified data format to facilitate subsequent processing and display by the system: the unified format includes status ID, status information, and detailed information. The detailed information is different and will not be displayed in detail: Step s25 (output account status): present the final determined account status result to the user or system for subsequent processing.

[0046] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes, and they can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

Claims

1. A method for determining a social network account status, characterized in that: include: S1: Get the status data of the target account; S2: extracting basic account information, account fan information and account speech information from the status data; S3: Compare the basic information of the account, the fan information of the account and the speech information of the account with preset conditions, and determine the status of the target account according to the comparison result.

2. The method for determining the status of a social network account according to claim 1, wherein: The account fan information includes at least the fan increase or decrease rate and fan type distribution, and the account speech information includes at least speech frequency, speech time period, speech content, interaction frequency, and interaction content.

3. The method for determining the status of a social network account according to claim 1, wherein: The preset condition is determined based on the historical data of the basic information of the account, the fan information of the account and the speech information of the account.

4. The method for determining the status of a social network account according to claim 2, wherein: Establishing a status judgment rule in advance using the preset condition, wherein the status judgment rule includes a corresponding relationship between the preset condition and the account status; In S3, the status of the target account is determined according to the comparison result and the status judgment rule.

5. The method for determining the status of a social network account according to claim 4, wherein: Also includes: S4: If the target account is in a normal state, extracting the interactive accounts and corresponding account types, interaction types, interaction times and interaction contents in multiple predetermined time periods; S5: establishing a homogeneous graph using the target account and the interactive account, and calculating an interactive index according to the account type, the interactive type, the number of interactions, and the interactive content; S6: constructing an interaction change curve using the interaction indexes of multiple predetermined time periods; S7: Determine whether the target account has potential abnormal risk according to the interaction change curve.

6. A device for determining a social network account status, characterized in that: include: The acquisition module is used to obtain the status data of the target account; A first extraction module, used to extract basic account information, account fan information and account speech information from the status data; The determination module is used to compare the basic information of the account, the fan information of the account and the speech information of the account with preset conditions, and determine the status of the target account according to the comparison result.

7. The social network account status determination device according to claim 6, characterized in that: The account fan information includes at least the fan increase or decrease rate and fan type distribution, and the account speech information includes at least speech frequency, speech time period, speech content, interaction frequency, and interaction content.

8. The device for determining the status of a social network account according to claim 6, wherein: The preset condition is determined based on the historical data of the basic information of the account, the fan information of the account and the speech information of the account.

9. The device for determining the status of a social network account according to claim 7, wherein: Establishing a status judgment rule in advance using the preset condition, wherein the status judgment rule includes a corresponding relationship between the preset condition and the account status; The status of the target account is determined according to the comparison result and the status judgment rule.

10. The device for determining the status of a social network account according to claim 9, wherein: Also includes: A second extraction module is used to extract the interactive accounts and corresponding account types, interaction types, interaction times and interaction contents in multiple predetermined time periods if the target account is in a normal state; A calculation module, used to establish a homogeneous graph using the target account and the interactive account, and calculate an interaction index according to the account type, the interaction type, the number of interactions and the interactive content; A construction module, using the interaction indexes of multiple predetermined time periods to construct an interaction change curve; The judgment module is used to judge whether the target account has potential abnormal risks according to the interaction change curve.

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