**5. Background and previous studies**

A survey of previous work review is carried out based on research papers on distinct approaches like questionnaires, profile information, status updates, groups or communities joined on platforms like Facebook, Twitter and LinkedIn. Schrammel et al. [19] reported the outcomes of web-based survey to examine the relationship between information shared by the individuals on online platforms, their usage patterns and personality attributes. 162 completed questionnaires were gathered from different countries around 44% of the people who participated in the study were graduated. Five factor personality scale questionnaire was used. In order to determine behavioral patterns, community specific questions were also included. The results displayed that individuals with high extraversion scores tends to have a greater number of connections. Meanwhile, agreeableness was not related to the number of connections. It was also observed that individuals investing more time online share more personal information on profile [19]. T. Ryan et al. conducted research on self-selected 1158 Facebook users and 166 Facebook non users where participants were aged between 18 and 44. A bundle of 124 questions comprising Big Five Inventory, SELSA-S and NPI-29 along with Facebook usage survey consisting 18 questions was conducted specifically for Facebook users. The data gathered from both Facebook users and non-users was divided into two groups in order to differentiate which characteristics will more likely belong to Facebook users and non-users [20]. B. Zhong et al. [21] in his study explored the relation between personality characteristics and time spent on internet for different purposes like social media, study and other activities. 436 University students participated in this study by answering survey questions on the total internet usage, multitasking experience, need for cognition, Information and Communication Technology (ICT) innovativeness. The one of the two main factors that can be considered in this study is that an individual might not be much prompted to handle and process information on social networking sites. Another factor is that an individual with high NFC could be more efficient and hence gives less time to social networking sites and vice versa [21].

Mohammad Dalvi-Esfahani et al. utilized personality traits as moderators to evaluate the impact of Perspective Taking (PT) and Empathic Concern (EC) on social media addiction (SMA) [22]. 592 high school students between 15 and 18 yrs. participated in this study. The data gathered was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). It was observed that behavior associated SMA was reflected more among high income schools [22]. Pavica Sheldon et al. aimed to determine whether Big five traits, contextual age indicators and fear of missing out are significant indicators of addictions associated with social networking sites. A survey of 337 students consisting of 193 women was conducted. In this study the social activity, interpersonal interaction and life satisfaction were estimated by The Rubin and Rubin life position scale. Big Five Inventory-10 was utilized to measure personality attributes and Bergen Facebook Addiction Scale (BFAS) was used to evaluate social media addiction. As a result, a positive correlation was realized between social activity and Snapchat. It was also observed that FOMO and social media addiction were highly correlated [23]. In a recent study conducted by J. Brailovskaia et al. the consequences of Covid-19 pandemic and its association with the social media usage were examined. A total 550 individuals participated where 76.2% were women. 65.3% of individuals belonged to the student category whereas the percentage of employed individuals was 33.5%. It was discovered that higher the burden of the pandemic, the lower will be the self-control which will ultimately lead to excessive social media usage [24] (**Table 2**).



**Table 2.**

*Personality Prediction using Questionnaire approach.*

Content generated by the user on Facebook was associated with the responses given by them in the questionnaire. The Facebook features used to determine personality traits in this research are- Total status updated, Photos uploaded, Total number of groups joined, Number of Facebook friends, Number of Facebook likes, Number of times the user is tagged in photos by other profiles.

Questionnaires are time consuming and there are high chances of a person not giving accurate answers to avoid being judged. Whereas, the usage-based approach only focuses on the time given by the user rather than the activity type. Hence in order to mitigate these limitations, some other aspects of social media usage were included. Another popular approach utilized to determine personality traits is to study user behavior on social media through certain level of activities like posting status, commenting, following certain groups/ communities.

In order to bridge the gap between social networking sites and personality traits with the help of machine learning, J. Golbeck et al. [26] utilized BFI (Big Five Personality Inventory) and Facebook profile information i.e. "About me" and status update of the individuals. Data was accumulated from 279 individuals meanwhile; linguistic analysis was performed on data gathered from only 167 individuals where at least 10 words in the text fields were present. Golnoosh Farnadi et al. [27] in his study utilized Facebook user generated data from myPersonality app to predict personality traits of an individual. 9917 status updates from 250 Facebook users, frequency as well as time of posting were collected from myPersonality project. One or more personality traits were assigned to the user based on the answers given by them in questionnaire. The four features leveraged in this study are LIWC feature (from status updates and other text), time related feature, social network feature (network size and density) and other features like number of statues per user, number of urls and words occurring more than once [27].

Amichai-Hamburger et al. and Vinitzky et al. in their research suggested a strong connection between user behavior on Facebook and personality for this purpose they targeted participants who tend to use Facebook often. The data was gathered from 237 Israeli university students comprising 136 female and 101 male with mean age 22 years. In order to conduct study in accordance with the five-factor model, students were asked to NEO-PI-R and a self-report measure. Apart from this, user generated content on Facebook was also measured and encoded. Four dimensions of users aimed were: basic information, personal information, contact information & education and work information. It was observed that highly extroverted people tends to share less personal information as they are more confident with their social skills than introverted people also, extroversion was positively correlated to the number of friends instead of groups joined. It was also found that highly neurotic individuals less likely post their pictures as compared to less neurotic people [28].

Michael Tadsse et al. conducted a research to predict the personality traits of Facebook users using Big Five model. myPersonality dataset was used with 250 Facebook users and 9917 Facebook updates. Features selected for analysis, in accordance with the personality attributes were- network size, density, transitivity, betweenness and brokerage. It was found that the extraversion personality trait along with SNA (Social Network Analysis) resulted in best prediction accuracy [29]. Y Bachrach et al. attempted to predict personality traits by analyzing Facebook activities of a user by their actions (posts, groups and likes) and their friends for e.g. tagging and size & density of the friendship network [30].

Content generated by the user on Facebook was associated with the responses given by them in the questionnaire. The Facebook features used to determine personality traits in this research are:

• Total status updated.

*Study of Approaches to Predict Personality Using Digital Twin DOI: http://dx.doi.org/10.5772/intechopen.110487*


Correlations were determined between these features utilized by producing plots known as 'Clustered Scatter Plots'. By including more Facebook features the accuracy of the model improvised. And a clear picture was observed to determine the correlation between each personality trait and Facebook feature for e.g., agreeableness was found to be positively correlated with groups joined, posts liked and total friends. This study primarily focused on the data of individuals who are active on social media and agreed to use the application for personality prediction. This sort of data might lead to selection bias. Also, the total number of posts liked, groups joined were taken into consideration but not the type of content liked or group joined (**Table 3**).



#### **Table 3.**

*Personality Prediction using content generated on Facebook in association with questionnaire.*
