A Recommender System for Online Social Network based Persona Analysis

IJSRD - International Journal for Scientific Research & Development| Vol. 3, Issue 08, 2015 | ISSN (online): 2321-0613

A Recommender System for Online Social Network based Persona Analysis Prof. Ranjeetsingh Suryawanshi1 Ankit Sharma2 Ajay Singh Rautela3 Akshay Trimbake4 Sanket Vangari5 1 Professor 2,3,4,5Student 1,2,3,4,5 Department of Computer Science 1,2,3,4,5 Trinity College of Engineering and Research, Pune Abstract—Our primary goal is to design a recommender system for analyzing the personality traits of people, in online social networks, for hiring potential candidates via an online recruiting system where the social and cultural aspects of a client will be assessed by the HRMs using data mined from posts, comments, etc. on/by the clients on social media.

Key words: Recommender System, Persona Analysis I. INTRODUCTION Human Resource Management (HRM) has recently shown an upcoming inflow of networking and network-based devices [1]. In this paper we primarily focus on designing a system that points out the personality traits of a Candidate for Human Resource Management activity.

II. EXISTING WORK Currently existing systems have their focus mainly on the concerns of providing a candidate who fits the job description based on the skills defined by the candidates in their resumes, CVs, etc.[1].None of these systems can ensure the cultural fitness of the candidate with respect to the hiring company’s social culture. This happens because that would require a thorough knowledge of the candidate’s personality traits that can only be measured when some time is spent with the candidate.

Over the past 10 years, social interactions using Online Social Networking Sites like Facebook and Twitter have become unavoidable. Regardless of their psychological productivity, OSNs have remained relatively unexplored from an experiential outlook.

This can be better explained by going through the Case Studies carried out on:  Personality and Self-Reported Facebook-Related Behaviors.  Personality and Observable Information on Facebook Profiles.

Currently, the most popular and widely used Online Social Networking site in the world is Facebook. The data on Facebook has been studied thoroughly, mapping out the connection between the OSN behavior and personality traits [3][4]. The objective of Study 1 was to examine the affiliation between personality and a domain of self-reported Facebook activities. It was found that people who spent more time socializing in the offline world also tend to spend more time socializing online [5]. People were assessed on the five factor model (FFM) which consists of personality dimensions (agreeableness, neuroticism, extraversion, conscientiousness, and openness to experience) [6].

Extraverts are people who seek other people’s attention and make their presence evident. The number of Facebook friends and comments on various pages show the extrovert

nature. Agreeableness is higher in people who viewed or visited more pages compared to people who visited less number of pages. People who spent more time viewing pages are low on conscientiousness. People with open nature (Openness) tend to upload or replace more photographs. No relations were found between Neuroticism and any of the self-reported Facebook behaviors [3].

In Study 2, the relation between the observable information found on Facebook profiles and personality traits are studied [4]. Extraversion and Openness are the only dimensions that were related to the information extracted from Facebook. The overall number of friends on Facebook unveiled the extravert nature of the individual and the number of friends in the limited or local network exhibited the openness [3]. Except Extraversion and Openness, all other dimensions were not related to the observable Facebook information.

An analysis carried on establishing of connections through social networks, tell us that social networks are made in a way that they support efficient local/global search and communication. Also, while giving this a thought, one might get ideas that might be applicable to social networking sites such as LinkedIn, Facebook, Myspace, etc., that are being used all over the globe.

The establishment of these conclusions is derived by studying a survey on basic positivity and negativity observed in posts and comments made on Online Social Networks as follows: A. Initial Data Search and Collection Survey The Survey started with the most popular Social Networking Sites (SNS), where initially focus was on number of wall posts. The chart shows the number of wall posts per forum of a random survey: Total Forum Positiv Negativ Survey wall Name e Posts e Posts Year posts Scientific 689,00 Januar 47% 22% American 3 y 2012 Reference Januar 90,269 66.7% 25.3% paper[9] y 2009 122 pewresearch.or 4 1.8 July million g million million 2014 words Table 1: Survey on Positive and Negative Posts [9].

After this, a search of words was performed, using both, positive keywords and negative keywords which led to the generation of posts from the keywords in the database [10].

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A Recommender System for Online Social Network based Persona Analysis (IJSRD/Vol. 3/Issue 08/2015/140)

B. Initial Text Mining and Pre-processing Many software’s / algorithms like Rapid-miner, WorldNet, and data collection and processing tree have been developed to test common positive and negative words.

(say craving online attention, getting negatively aroused when people do not comment on their status update, retaliation to negativity and criticism in comments) and displayed in front of the HRM to aid them in coming to a decision about hiring such candidates or not. The HR will then conclude whether the attitude and culture of the candidate, based on traits gathered by the mined data, from the OSN, to be fit or unfit for their organization and their required cultural standards.

IV. CONCLUSION

Fig.1 Data collection and processing tree [10].

The process starts by first uploading the data into the first stage of Read Excel. The uploaded data will then be processed in the second stage of Process Documents using sub stages like Extract Content, Tokenize, Transform Cases, Filter Stop-words, Filter Tokens, respectively. The Processed Data from the final stage will have the sets of final word lists.

C. Tagging Text Data The final word lists will be pruned using the above mentioned methods, with the results displayed in and as Table 1, in a separate column of both positive and negative words.

Positive Negative Agree Bad Appreciate Cannot Beneficial Damage Comfort Dangerous Ease Depression Easier Died Enjoy Difficult Good Error Great Hard Greatest Failure Help Impossible Hope Lack Important No Yes Not Improve Poor Thanks Weak Table 2: Positive and Negative Classification of Words from Posts III. PROPOSED SYSTEM Our main aim is to point out the qualities or flaws in the candidate’s personality and behaviour. For this we will design a recommender system that will fetch the data from the posts, comments, etc., of a candidate and put forth the HR. After conducting various psychological and sociological analysis [7][8] on the mined/fetched data, a conclusion will be drawn about the candidate’s practices in self-promotion on Social Networks (e.g. posting status updates, photos and updating profile data) and at the same time a wide variety of anti-socialistic behaviours and exhibition of narcissistic personality traits [2] will be studied

The data gathered from the above mentioned surveys, case studies and analysis lead to an end conclusion that online social networks have now become entwined in man’s life in such a way that if its power is harnessed fruitfully, the results can be miraculous. One such outcome of social network based behavioral analysis [1] is in the use of the recruitment process by the HRMs, who could use the data mined from the social network profile of a candidate to judge their social and cultural fitness in a professional environment. This will make the occurrence of post recruitment issues drastically less in comparison to the standard recruitment process based issues.

ACKNOWLEDGEMENTS We take this opportunity to express our heartfelt gratitude and deep regards to our Project guide, Prof. Ranjeetsingh Suryawanshi, for his impeccable guidance, monitoring and constant motivation throughout the process of writing this research paper.

The blessing, help and guidance given by him shall from time to time, carry us a long way on the journey of life which we are about to embark.

We are also obliged to Prof. Rakhi Bhardwaj, our Project Coordinator, for the valuable information and guidance provided by her.

REFERENCES [1] Ricardo Buettner,”A Framework for Recommender Systems in Online Social Network Recruiting: An Interdisciplinary Call to Arms”, IEEE, 2014 [2] E. Y. Ong, R. P. Ang, J. C. Ho, J. C. Lim, D. H. Goh, C. S. Lee, and A. Y. Chua, “Narcissism, extraversion and adolescents’ self-presentation on Facebook,” Pers Individ Dif, 2011.

[3] S. D. Gosling, A. A. Augustine, S. Vazire, N. Holtzman, and S. Gaddis, “Manifestations of Personality in Online Social Networks: Self-Reported Facebook Related Behaviors and Observable Profile Information,” Cyber-psychology, Behavior, And Social Networking, 2011.

[4] Reis HT, Gosling SD. (2010). Social psychological methods outside the laboratory. In Fiske ST, Gilbert DT, Lindzey G, eds. Handbook of social psychology.

5th ed., vol. 1. New York: Wiley, pp. 82–114.

[5] Ashton MC, Lee K, Paunonen SV, “What is the central feature of extraversion? Social attention versus reward sensitivity”, Journal of Personality and Social Psychology 2002; 83:245–252.

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[6] Gosling SD, Rentfrow PJ, Swann WB Jr., “A very brief measure of the Big Five personality domains”, Journal of Research in Personality 2003; 37:504–528.

[7] J. J. A. Denissen, R. Geenen, M. Selfhout, and M.A. G.van Aken, “ Single-item Big Five Ratings in a Social Network Design,” Eur J Personality,vol. 22, no. 1, pp.

37-54, 2008.

[8] S. D. Gosling, S. Gaddis, and S. Vazire, “Personality Impressions Based on Facebook Profiles,” in ICWSM ’07 Proc. AAAI, 2007, pp. 1–4.

[9] Bimal Vishwanath Alan Mislove, Meeyoung Cha, Krishna P. Gummadi. “On the Evolution of User Interaction in Facebook”.

[10] Altug Akay, Andrei Dragomir and Bj ö rn­Erik Erlandsson, Senior Member, IEEE. “Network­Based Modeling and Intelligent Data Mining of Social Media for Improving Care”, IEEE Journal of Biomedical and Health Informatics, Vol. 19, No. 1, January 2015.

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