WO2023079369A1

WO2023079369A1 - System and method for analysing dimensions of influence on social media platforms

Info

Publication number: WO2023079369A1
Application number: PCT/IB2022/050036
Authority: WO
Inventors: Gayatri Sapru
Original assignee: Gayatri Sapru
Priority date: 2021-11-03
Filing date: 2022-01-04
Publication date: 2023-05-11

Abstract

A system (100) for analysing dimensions of influence is disclosed. An inquiry selection module (110) selects a predetermined depth of inquiry to set a sample size for one or more social media contents affiliated with at least one of an individual or a brand. A data collection module (120) collects the one or more social media contents. A data classification module (130) extracts one or more keywords from the one or more social media contents using a keyword extraction technique, interprets an influence of the one or more social media contents, categorizes the one or more social media contents into a plurality of dimensions of influence and a plurality of corresponding sub-dimensions. A map generation module (140) generates a map of influence with one or more peaks and troughs for the at least one of the individual or the brand based on the plurality of dimensions of influence of the one or more social media contents.

Description

SYSTEM AND METHOD FOR ANALYSING DIMENSIONS OF INFLUENCE ON SOCIAL MEDIA PLATFORMS

EARLIEST PRIORITY DATE:

This Application claims priority from a Complete patent application filed in India having Patent Application No. 202121050602, filed on November 03, 2021 and titled “SYSTEM AND METHOD FOR ANALYSING DIMENSIONS OF INFLUENCE ON SOCIAL MEDIA PLATFORMS”.

BACKGROUND

Embodiments of the present disclosure relate to a marketing management system and more particularly to a system and a method for analysing dimensions of influence on social media platforms.

Recently last two decades have witnessed an explosion of technological innovation in realm of communication, with social media fundamentally altering ways of expressing and experiencing the world around us in a way that neither print, radio, or television were able to do. A key shift has been the proliferation of the social media platforms that provide a common forum for interacting with celebrities and fellow fans, creating an active audience that makes meanings in real time through assessing social media content and shared opinions rather than simply receiving well curated messages without being able to engage with them in a dialogue.

A chief effect of this revolution has been on a concept of influence -defined as 'compelling behaviour change without threat of punishment or promise of reward which results largely from respect and esteem in which one is held by others. Until now, the influence has been understood by social and political scientists as the result of sustained and curated efforts over a long period of time, chiefly through acts that display wealth or power or goodwill in a community. However, the study of influence has largely been seen as either a subset of public or international relations. There is a huge monetary, political and public policy outcome of social media opinions and the influence, which has not yet been treated as a matter of serious inquiry. This alone makes the influence on social media worth of study because the influence created by individuals or brands on the social media becomes real world capital through the sale of media, merchandise, branded products and the like. Various systems are available which measures the influence that dictates success of the influencer/brand/personality and any consequent brand or business that affiliates with them

Conventionally, the system available for measuring the online influence includes measuring quantitative influence such as how many followers or viewers a brand of influencer or an individual has for instance. However, such conventional system ignores measuring quality of the influence that dictates success of the influencer or brand or personality and any consequent brand or business that affiliates with them. Moreover, such a conventional system is unable to study influence in terms of its dimensions, value to varied industries, or standardized models of understanding true conversion of influence to commercial goals.

Hence, there is a need for an improved system and a method for analysing dimensions of influence on social media platforms in order to address the aforementioned issues.

BRIEF DESCRIPTION

In accordance with an embodiment of the present disclosure, a system for analysing dimensions of influence on social media platforms is disclosed. The system includes a processing subsystem hosted on a server. The processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes an inquiry selection module configured to select a predetermined depth of inquiry to set a sample size for one or more social media contents affiliated with at least one of an individual or a brand. The processing subsystem also includes a data collection module operatively coupled to the inquiry selection module. The data collection module is configured to collect the one or more social media contents affiliated with the at least one of the individual or the brand from one or more social media platforms for a selected predetermined depth of query. The processing subsystem also includes a data classification module operatively coupled to the data collection module. The data classification module is configured to extract one or more keywords from the one or more social media contents using a keyword extraction technique upon pre-processing of the one or more social media contents collected. The data classification module is also configured to interpret an influence of the one or more social media contents based on the one or more keywords extracted using a sentiment analysis technique, a topic labelling technique and an intent detection technique. The data classification module is also configured to categorize the one or more social media contents into a plurality of dimensions of influence and a plurality of corresponding sub-dimensions by utilizing a trained hybrid model based on an interpretation of the influence. The processing subsystem also includes a map generation module operatively coupled to the data classification module. The map generation module is configured to generate a map of influence with one or more peaks and troughs for the atleast one of the individual or the brand based on the plurality of dimensions of influence of the one or more social media contents.

In accordance with another embodiment of the present disclosure, a method for analysing dimensions of influence on social media platforms is disclosed. The method includes selecting, by an inquiry selection module of a processing subsystem, a predetermined depth of inquiry to set a sample size for one or more social media contents affiliated with at least one of an individual or a brand. The method also includes collecting, by a data collection module of the processing subsystem, the one or more social media contents affiliated with the at least one of the individual or the brand from one or more social media platforms for a selected predetermined depth of query. The method also includes extracting, by a data classification module of the processing subsystem, one or more keywords from the one or more social media contents using a keyword extraction technique upon pre-processing of the one or more social media contents collected. The method also includes interpreting, by the data classification module of the processing subsystem, an influence of the one or more social media contents based on the one or more keywords extracted using a sentiment analysis technique, a topic labelling technique and an intent detection technique. The method also includes categorizing, by the data classification module of the processing subsystem, the one or more social media contents into a plurality of dimensions of influence and a plurality of corresponding sub-dimensions by utilizing a trained hybrid model based on an interpretation of the influence. The method also includes generating, by a map generation module of the processing subsystem, a map of influence with one or more peaks and troughs for the atleast one of the individual or the brand based on the plurality of dimensions of influence of the one or more social media contents. To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:

FIG. 1 is a block diagram of a system for analysing dimensions of influence on social media platforms in accordance with an embodiment of the present disclosure;

FIG. 2 is a schematic representation of an exemplary embodiment of a system for analysing dimensions of influence on social media platforms of FIG. 1 in accordance with an embodiment of the present disclosure;

FIG. 3 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure; and

FIG. 4 is a flow chart representing the steps involved in a method for analysing dimensions of influence on social media platforms in accordance with an embodiment of the present disclosure.

Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein. DETAILED DESCRIPTION

For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.

The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or sub-systems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures or additional components. Appearances of the phrase "in an embodiment", "in another embodiment" and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.

In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.

Embodiments of the present disclosure relate to a system and a method for analysing dimensions of influence on social media platforms. The system includes a processing subsystem hosted on a server. The processing subsystem is configured to execute on a network to control bidirectional communications among a plurality of modules. The processing subsystem includes an inquiry selection module configured to select a predetermined depth of inquiry to set a sample size for one or more social media contents affiliated with at least one of an individual or a brand. The processing subsystem also includes a data collection module operatively coupled to the inquiry selection module. The data collection module is configured to collect the one or more social media contents affiliated with the at least one of the individual or the brand from one or more social media platforms for a selected predetermined depth of query. The processing subsystem also includes a data classification module operatively coupled to the data collection module. The data classification module is configured to extract one or more keywords from the one or more social media contents using a keyword extraction technique upon pre-processing of the one or more social media contents collected. The data classification module is also configured to interpret an influence of the one or more social media contents based on the one or more keywords extracted using a sentiment analysis technique, a topic labelling technique and an intent detection technique. The data classification module is also configured to categorize the one or more social media contents into a plurality of dimensions of influence and a plurality of corresponding sub-dimensions by utilizing a trained hybrid model based on an interpretation of the influence. The processing subsystem also includes a map generation module operatively coupled to the data classification module. The map generation module is configured to generate a map of influence with one or more peaks and troughs for the atleast one of the individual or the brand based on the plurality of dimensions of influence of the one or more social media contents.

FIG. 1 is a block diagram of a system (100) for analysing dimensions of influence on social media platforms in accordance with an embodiment of the present disclosure. The system (100) includes a processing subsystem (105) hosted on a server (108). In one embodiment, the server (108) may include a cloud server. In another embodiment, the server (108) may include a local server. The processing subsystem (105) is configured to execute on a network (not shown in FIG. 1) to control bidirectional communications among a plurality of modules. In one embodiment, the network may include a wired network such as local area network (LAN). In another embodiment, the network may include a wireless network such as Wi-Fi, Bluetooth, Zigbee, near field communication (NFC), infra-red communication (RFID) or the like.

The processing subsystem (105) includes an inquiry selection module (110) configured to select a predetermined depth of inquiry to set a sample size for one or more social media contents affiliated with at least one of an individual or a brand. As used herein, the term ‘sample size’ is defined as total of all posts on selected platforms in selected period. In one embodiment, the predetermined depth of inquiry may include at least one of number of posts, meaning of posts, selection of one or more social media platforms, selection of time period or a combination thereof. In such embodiment, the meaning of the posts may include, but not limited to, a historical post, a recent post, a lifetime post and the like. In another embodiment, the one or more social media platforms may include, but not limited to, YouTube ™, Facebook ™, Instagram ™, Twitter ™ and the like. In some embodiment, the time period may include, but not limited to, 3 months, 6 months, 1 year, 2 year, recent and the like.

The processing subsystem (105) also includes a data collection module (120) operatively coupled to the inquiry selection module (110). The data collection module (120) is configured to collect the one or more social media contents affiliated with the at least one of the individual or the brand from one or more social media platforms for a selected predetermined depth of query. In one embodiment, the one or more social media contents may include at least one of a post, a feed, a blog, an article, a video, an image, a contest, a giveaway, a market survey, a poll, a feedback, a comment, a product review, a story, a news post in a news channel or a combination thereof. In such embodiment, the one or more social media contents are collected from the one or more social media platforms in one or more formats such as a text format, an image format, a video format or an audio format. In a specific embodiment, the one or more social media contents are collected from the one or more social media platforms via web crawling, web scraping and the like.

The processing subsystem (105) also includes a data classification module (130) operatively coupled to the data collection module (120). The data classification module (130) is also configured to pre-process the one or more social media contents by performing at least one of data cleaning, data transformation of the one or more formats of the one or more social media contents into a predefined format or a combination thereof. The data classification module (130) is configured to extract one or more keywords from the one or more social media contents using a keyword extraction technique upon pre-processing of the one or more social media contents collected. As used herein, the term ‘keyword extraction technique’ is defined as an automated process of extracting the most relevant words and expressions from text. In one embodiment, the keyword extraction technique may include at least one of a parts of speech (POS) tagging technique, a rapid automatic keyword extraction (RAKE) technique, a collocations and co-occurrences-based technique, a term frequencyinverse document frequency (TF-IDF) technique or a combination thereof.

The data classification module (130) is also configured to interpret an influence of the one or more social media contents based on the one or more keywords extracted using a sentiment analysis technique, a topic labelling technique and an intent detection technique. As used herein, the term ‘influence’ is defined as a marketing term that describes an individual's ability to affect other people's thinking in a social online community. Similarly, the term ‘sentiment analysis technique’ is defined as a contextual mining of text which identifies and extracts subjective information in source material and helping a business to understand the social sentiment of their brand, product or service while monitoring online conversations. Again, the term ‘topic labelling technique’ is defined as a natural language processing (NEP) technique that allows an individual to automatically extract meaning from text by identifying recurrent themes or topics. Further the term ‘intent detection technique’ is defined as a process of understanding a user's end goal or objective given what they have said or typed in any task oriented conversational system.

The data classification module (130) is also configured to categorize the one or more social media contents into a plurality of dimensions of influence and a plurality of corresponding sub-dimensions by utilizing a trained hybrid model based on an interpretation of the influence. As used herein, the term ‘hybrid model’ is defined as a machine learning model which is developed for performing certain tasks by combination of a rule-based classifier and machine learning technology-based system. The hybrid machine learning model is developed and trained against plurality of datasets for categorizing the one or more social media contents. In this, human intervention along with the machine learning technology-based system helps in the categorization of the one or more social media contents. In one embodiment, the plurality of dimensions may include an ideology, a skill, an information, a relatability and a commercial impact. In such embodiment, the plurality of sub-dimensions corresponding to the ideology category may include, but not limited to, opinion, beliefs, activism and the like. In another embodiment, the plurality of sub-dimensions corresponding to the skill category may include, but not limited to, talent, rivals, innovation, review and the like. In yet another embodiment, the plurality of subdimensions corresponding to the information category may include at least one of news, updates, teaching, tips, guidance or a combination thereof. In one embodiment, the plurality of sub-dimensions corresponding to the relatability category may include at least one of familiar, comfortable, aspirational, ideal, response to feedback or a combination thereof. In another embodiment, the plurality of sub-dimensions corresponding to the commercial impact category may include, but not limited to, channels, reach and engagement, promotions, merchandise and the like.

The processing subsystem (105) also includes a map generation module (140) operatively coupled to the data classification module (130). The map generation module (140) is configured to generate a map of influence with one or more peaks and troughs for the atleast one of the individual or the brand based on the plurality of dimensions of influence of the one or more social media contents. In one embodiment, the map of influence may include topography of their influence - ‘zones’ of different sizes relative to their influence in those topics or matters, peaks and troughs of strength and weakness and the like. As used herein, the term ‘map of influence’ is defined as the dimensions of influence where there are highs and lows and therefore room for improved strategy and optimization. The map generation module (140) learns from and adds to its database from the map of influence generated for different users, growing its own indexing and clustering capabilities with increase in users. In due course this would lead to a way to create comparison and baselines for influence index.

In a particular embodiment, the processing subsystem (105) further includes a recommendation generation module (150) configured to generate one or more recommendations for accomplishment of influence optimisation based on the map of influence generated. The one or more recommendations may include a formula, a plan to increase or maintain ‘levels across parameters which can be custom created based on the direction in which the brand/influencer wants to increase or decrease their influence. Influence architecture thus maps and sets a course for the brand/influencer in the short term or long term as per their requirement. FIG. 2 is a schematic representation of an exemplary embodiment of a system (100) for analysing dimensions of influence on social media platforms of FIG. 1 in accordance with an embodiment of the present disclosure. Considering an example, wherein the system (100) is utilized by a company to analyse dimensions of influence on the social media platforms. The system (100) helps in understanding what type of influence is being exerted by an individual personality or business/brand online, what categories does this influence extend to and to what degree and the like.

For measuring the online influence, an inquiry selection module (110) of the system (100) selects a predetermined depth of inquiry to set a sample size for one or more social media contents affiliated with at least one of an individual or a brand. For example, the predetermined depth of inquiry may include at least one of number of posts, meaning of posts, selection of one or more social media platforms (102), selection of time period or a combination thereof. In such embodiment, the meaning of the posts may include, but not limited to, a historical post, a recent post, a lifetime post and the like. In another embodiment, the one or more social media platforms may include, but not limited to, YouTube ™, Facebook ™, Instagram ™, Twitter ™ and the like. In some embodiment, the time period may include, but not limited to, 3 months, 6 months, 1 year, 2 year, recent and the like.

Here, the inquiry selection module (110) is located on a processing subsystem (105) which is hosted on a cloud server (108). The processing subsystem (105) is configured to execute on a network (115) to control bidirectional communications among a plurality of modules. Once, the predetermined depth of inquiry is selected, a data collection module (120) collects the one or more social media contents affiliated with the at least one of the individual or the brand from one or more social media platforms for a selected predetermined depth of query. In the example used herein, the one or more social media contents may include at least one of a post by the influencer in of the social media platform. For example, the post posted by the influencer is related to price of cryptocurrency.

Based on the social media content collected, a data classification module (130) first pre-processes the social media content by performing at least one of data cleaning, data transformation for converting the one or more formats of the social media content into a predefined format. The data classification module (130) is configured to extract one or more keywords from the social media content using a keyword extraction technique upon pre-processing. For example, the keyword extraction technique may include at least one of a parts of speech (POS) tagging technique, a rapid automatic keyword extraction (RAKE) technique, a collocations and co-occurrences-based technique, a term frequency-inverse document frequency (TF-IDF) technique or a combination thereof.

The data classification module (130) is also configured to interpret an influence of the social media content based on the one or more extracted keywords using a sentiment analysis technique, a topic labelling technique and an intent detection technique. Further, the data classification module (130) is also configured to categorize the social media content into a plurality of dimensions of influence and a plurality of corresponding sub-dimensions by utilizing a trained hybrid model based on an interpretation of the influence. Here, the hybrid model is developed and trained against plurality of datasets for categorizing the social media content. In this, human intervention along with the machine learning technology-based system helps in the categorization of the social media content. In the example used herein the plurality of dimensions may include an ideology, a skill, an information, a relatability and a commercial impact. In such an example, the plurality of sub-dimensions corresponding to the ideology category may include, but not limited to, opinion, beliefs, activism and the like. Similarly, the plurality of sub-dimensions corresponding to the skill category may include, but not limited to, talent, rivals, innovation, review and the like. Again, the plurality of sub-dimensions corresponding to the information category may include at least one of news, updates, teaching, tips, guidance or a combination thereof. Again, the plurality of sub-dimensions corresponding to the relatability category may include at least one of familiar, comfortable, aspirational, ideal, response to feedback or a combination thereof. In addition, the plurality of subdimensions corresponding to the commercial impact category may include, but not limited to, channels, reach and engagement, promotions, merchandise and the like.

Based on categorization of the social media content into the plurality of dimension of influence, a map generation module (140) generates a map of influence with one or more peaks and troughs for the atleast one of the individual or the brand to understand the influence levels. The map generation module (140) learns from and adds to its database from the map of influence generated for different users, growing its own indexing and clustering capabilities with increase in users. In due course this would lead to a way to create comparison and baselines for influence index.

Further, the processing subsystem (105) also includes a recommendation generation module (150) configured to generate one or more recommendations for accomplishment of influence optimisation based on the map of influence generated. The one or more recommendations may include a formula, a plan to increase or maintain ‘levels across parameters which can be custom created based on the direction in which the brand/influencer wants to increase or decrease their influence.

FIG. 3 is a block diagram of a computer or a server in accordance with an embodiment of the present disclosure. The server (200) includes processor(s) (230), and memory (210) operatively coupled to the bus (220). The processor(s) (230), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.

The memory (210) includes several subsystems stored in the form of executable program which instructs the processor (230) to perform the method steps illustrated in FIG. 1. The memory (210) includes a processing subsystem (105) of FIG.l. The processing subsystem (105) further has following modules: an inquiry selection module (110), a data collection module (120), a data classification module (130), a map generation module (140) and a recommendation generation module (150).

The inquiry selection module (110) configured to select a predetermined depth of inquiry to set a sample size for one or more social media contents affiliated with at least one of an individual or a brand. The data collection module (120) is configured to collect the one or more social media contents affiliated with the at least one of the individual or the brand from one or more social media platforms for a selected predetermined depth of query. The data classification module (130) is configured to extract one or more keywords from the one or more social media contents using a keyword extraction technique upon pre-processing of the one or more social media contents collected. The data classification module (130) is also configured to interpret an influence of the one or more social media contents based on the one or more keywords extracted using a sentiment analysis technique, a topic labelling technique and an intent detection technique. The data classification module (130) is also configured to categorize the one or more social media contents into a plurality of dimensions of influence and a plurality of corresponding sub-dimensions by utilizing a trained hybrid model based on an interpretation of the influence. The map generation module (140) is configured to generate a map of influence with one or more peaks and troughs for the atleast one of the individual or the brand based on the plurality of dimensions of influence of the one or more social media contents. The recommendation generation module (150) is configured to generate one or more recommendations for accomplishment of influence optimisation based on the map of influence generated. The one or more recommendations may include a formula, a plan to increase or maintain levels across parameters which can be custom created based on the direction in which the brand/influencer wants to increase or decrease their influence.

The bus (220) as used herein refers to be internal memory channels or computer network that is used to connect computer components and transfer data between them. The bus (220) includes a serial bus or a parallel bus, wherein the serial bus transmits data in bit-serial format and the parallel bus transmits data across multiple wires. The bus (220) as used herein, may include but not limited to, a system bus, an internal bus, an external bus, an expansion bus, a frontside bus, a backside bus and the like.

FIG. 4 is a flow chart representing the steps involved in a method (300) for analysing dimensions of influence on social media platforms in accordance with an embodiment of the present disclosure. The method (300) includes selecting, by an inquiry selection module of a processing subsystem, a predetermined depth of inquiry to set a sample size for one or more social media contents affiliated with at least one of an individual or a brand in step 310. In one embodiment, selecting the predetermined depth of inquiry to set the sample size for the one or more social media contents may include selecting at least one of number of posts, meaning of posts, selection of one or more social media platforms, selection of time period or a combination thereof. In such embodiment, the meaning of the posts may include, but not limited to, a historical post, a recent post, a lifetime post and the like. In another embodiment, the one or more social media platforms may include, but not limited to, YouTube ™, Facebook ™, Instagram ™, Twitter ™ and the like. In some embodiment, the time period may include, but not limited to, 3 months, 6 months, 1 year, 2 year, recent and the like.

The method (300) also includes collecting, by a data collection module of the processing subsystem, the one or more social media contents affiliated with the at least one of the individual or the brand from one or more social media platforms for a selected predetermined depth of query in step 320. In one embodiment, collecting the one or more social media contents may include collecting at least one of a post, a feed, a blog, an article, a video, an image, a contest, a giveaway, a market survey, a poll, a feedback, a comment, a product review, a story or a combination thereof. In such embodiment, the one or more social media contents are collected from the one or more social media platforms in one or more formats such as a text format, an image format, a video format or an audio format. In a specific embodiment, the one or more social media contents are collected from the one or more social media platforms via web crawling, web scraping and the like.

The method (300) also includes extracting, by a data classification module of the processing subsystem, one or more keywords from the one or more social media contents using a keyword extraction technique upon pre-processing of the one or more social media contents collected in step 330. In some embodiment, extracting the one or more keywords from the one or more social media contents using the keyword extraction technique may include extracting the one or more keywords using at least one of a parts of speech (POS) tagging technique, a rapid automatic keyword extraction (RAKE) technique, a collocations and co-occurrences-based technique, a term frequency -inverse document frequency (TF-IDF) technique or a combination thereof.

The method (300) also includes interpreting, by the data classification module of the processing subsystem, an influence of the one or more social media contents based on the one or more keywords extracted using a sentiment analysis technique, a topic labelling technique and an intent detection technique in step 340. The method (300) also includes categonzing, by the data classification module of the processing subsystem, the one or more social media contents into a plurality of dimensions of influence and a plurality of corresponding sub-dimensions by utilizing a trained hybrid model based on an interpretation of the influence in step 350. In one embodiment, categorizing the one or more social media contents into the plurality of dimensions of influence may include categorizing the one or more social media contents into an ideology, a skill, an information, a relatability and a commercial impact categories. In such embodiment, the plurality of sub-dimensions corresponding to the ideology category may include, but not limited to, opinion, beliefs, activism and the like. In another embodiment, the plurality of sub-dimensions corresponding to the skill category may include, but not limited to, talent, rivals, innovation, review and the like. In yet another embodiment, the plurality of sub-dimensions corresponding to the information category may include at least one of news, updates, teaching, tips, guidance or a combination thereof. In one embodiment, the plurality of sub- dimensions corresponding to the relatability category may include at least one of familiar, comfortable, aspirational, ideal, response to feedback or a combination thereof. In another embodiment, the plurality of sub-dimensions corresponding to the commercial impact category may include, but not limited to, channels, reach and engagement, promotions, merchandise and the like.

The method (300) also includes generating, by a map generation module of the processing subsystem, a map of influence with one or more peaks and troughs for the atleast one of the individual or the brand based on the plurality of dimensions of influence of the one or more social media contents in step 360. In a specific embodiment, the method further includes generating, by a recommendation generation module, one or more recommendations for accomplishment of influence optimisation based on the map of influence generated. The one or more recommendations may include a formula, a plan to increase or maintain ‘levels across parameters which can be custom created based on the direction in which the brand/influencer wants to increase or decrease their influence.

Various embodiments of the present disclosure provides a system which studies influence in terms of its dimensions, value to varied industries, or standardized models of understanding true conversion of influence to commercial goals. Moreover, the present disclosed system measures quality of the influence that dictates success of the influencer/brand/personality and any consequent brand or business that affiliates with them as the quality of influence have a higher impact on their followers in terms of changing opinions such as criticism, purchase decisions, activism than a more quantitatively popular brand/influence.

Furthermore, the present disclosed system measures quality of influence as well as provides a scale of needs that are being met and unmet by the influencer/brand/personality across dimensions such as activism, motivation, resonance, among others.

In addition, the present disclosed system using a combination of keyword extraction, NLP and machine learning technology, develops a model which is trained to pick up on specific keywords to classify dimensions of influence.

It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the disclosure and are not intended to be restrictive thereof.

While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.

The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.

Claims

WE CLAIM:

1. A system (100) for analysing dimensions of influence on a social media platform comprising: a processing subsystem (105) hosted on a server (108), wherein the processing subsystem (105) is configured to execute on a network to control bidirectional communications among a plurality of modules comprising: an inquiry selection module (110) configured to select a predetermined depth of inquiry to set a sample size for one or more social media contents affiliated with at least one of an individual or a brand; a data collection module (120) operatively coupled to the inquiry selection module (110), wherein the data collection module (120) is configured to collect the one or more social media contents affiliated with the at least one of the individual or the brand from one or more social media platforms for a selected predetermined depth of query; a data classification module (130) operatively coupled to the data collection module (120), wherein the data classification module (130) is configured to: extract one or more keywords from the one or more social media contents using a keyword extraction technique upon preprocessing of the one or more social media contents collected; interpret an influence of the one or more social media contents based on the one or more keywords extracted using a sentiment analysis technique, a topic labelling technique and an intent detection technique; and categorize the one or more social media contents into a plurality of dimensions of influence and a plurality of corresponding sub-dimensions by utilizing a trained hybrid model based on an interpretation of the influence; and a map generation module (140) operatively coupled to the data classification module (130), wherein the map generation module (140) is configured to generate a map of influence with one or more peaks and troughs for the atleast one of the individual or the brand based on the plurality of dimensions of influence of the one or more social media contents.

2. The system (100) as claimed in claim 1, wherein the predetermined depth of inquiry comprises at least one of number of posts, meaning of posts, selection of one or more social media platforms, selection of time period or a combination thereof.

3. The system (100) as claimed in claim 1, wherein the one or more social media contents comprises at least one of a post, a feed, a blog, an article, a video, an image, a contest, a giveaway, a market survey, a poll, a feedback, a comment, a product review, a story or a combination thereof.

4. The system (100) as claimed in claim 1, wherein the one or more social media contents are collected from the one or more social media platforms in one or more formats comprising a text format, an image format, a video format or an audio format.

5. The system (100) as claimed in claim 1, wherein the keyword extraction technique comprises at least one of a parts of speech tagging technique, a rapid automatic keyword extraction technique, a collocations and co-occurrences-based technique, a term frequency-inverse document frequency technique or a combination thereof.

6. The system (100) as claimed in claim 1, wherein data classification module (130) is also configured to pre-process the one or more social media contents by performing at least one of data cleaning, data transformation of the one or more formats of the one or more social media contents into a predefined format or a combination thereof.

7. The system (100) as claimed in claim 1, wherein the plurality of dimensions of influence comprises an ideology, a skill, an information, a relatability and a commercial impact.

8. The system (100) as claimed in claim 1, wherein the plurality of corresponding sub-dimensions comprises at least one of opinion, beliefs, activism, talent, innovation, reviews, news, teaching, tips and guidance, familiar, comfortable, aspirational, ideal, response to feedback, channels, reach and engagement, promotions, merchandise or a combination thereof.

9. The system (100) as claimed in claim 1, wherein the processing subsystem (105) comprises a recommendation generation module (150) configured to generate one or more recommendations for accomplishment of influence optimisation based on the map of influence generated.

10. A method (300) comprising: selecting, by an inquiry selection module of a processing subsystem, a predetermined depth of inquiry to set a sample size for one or more social media contents affiliated with at least one of an individual or a brand (310); collecting, by a data collection module of the processing subsystem, the one or more social media contents affiliated with the at least one of the individual or the brand from one or more social media platforms for a selected predetermined depth of query (320); extracting, by a data classification module of the processing subsystem, one or more keywords from the one or more social media contents using a keyword extraction technique upon pre-processing of the one or more social media contents collected (330); interpreting, by the data classification module of the processing subsystem, an influence of the one or more social media contents based on the one or more keywords extracted using a sentiment analysis technique, a topic labelling technique and an intent detection technique (340);

19 categorizing, by the data classification module of the processing subsystem, the one or more social media contents into a plurality of dimensions of influence and a plurality of corresponding sub-dimensions by utilizing a trained hybrid model based on an interpretation of the influence (350); and generating, by a map generation module of the processing subsystem, a map of influence with one or more peaks and troughs for the atleast one of the individual or the brand based on the plurality of dimensions of influence of the one or more social media contents (360).

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