A Systematic Evaluation of Big Data Analytics Methodologies, Unresolved Concerns, Future Directions
Keywords:
Social Networks, Big Data, Content, Sentiment, Systematic Literature Review, Machine Learning, Natural Language Processing, Online Social Network, Personality Test, Profiling, Sentimental Analysis, Twitter Are Some Of The Keywords That Might Be Associated With This ArticleAbstract
SNSs link individuals globally, where they share material, photographs,
videos, thoughts, comments, friends. Social networks are defined
by velocity, volume, value, diversity, truth. Social Network Analysis
uses large data analytic methods and frameworks (SNA). With the
advent of social networks, using social data to explain communication
patterns and analyze user behavior has gained focus. In this research,
we show how big data analytics meets social media and examine 74
publications published between 2013 to August 2020 on big data
analytic methodologies in social networks. This report presents results
on important journals/conferences, annual distributions, publisher
distributions. Big data analytic methodologies are categorized as
content-oriented and network-oriented. Main concepts, evaluation
parameters, tools, evaluation methodologies, benefits, limitations are
explored. Open difficulties and future directions are examined. Usage
over Internet has significantly increased during the last few decades.
People sparing more time on social media services. Social media is a place
where users present themselves to the world, revealing personal details
and insights into their lives. We are starting to comprehend how some
of this information might be employed to better the users’ experiences
with interfaces and with one another. The widespread use of social
media sites results in an increase in both the quantity and amount of
data. The quantity of data that is submitted to these social networking
platforms is expanding day by day. This is preliminary work to model
the user patterns and to study the effectiveness of machine learning
active modeling approaches on the leading social networking service
Facebook. We created a model of the user comment patterns found
on Facebook Pages and used it to make a prediction about the number
of comments each post is likely to receive within the next 24 hours.
References
A. Kamilaris, A. Pitsillides, Social networking of the smart home,in: Personal Indoor and Mobile Radio Communications (PIMRC),2010 IEEE 21st International Symposium on, 2010, pp. 2632–2637.
doi:10.1109/PIMRC.2010.5671783.
K. Shvachko, H. Kuang, S. Radia, R. Chansler, The hadoopdistributedfile system, in: Mass Storage Systems and Technologies(MSST), 2010 IEEE 26th Symposium on, 2010, pp. 1–10.doi:10.1109/MSST.2010.5496972.
I. Polato, R. R´e, A. Goldman, F. Kon, A comprehensive view ofhadoopresearcha systematic literature review, Journal of Network andComputer Applications 46 (2014) 1–25.
T. Reuter, P. Cimiano, L. Drumond, K. Buza, L. Schmidt-Thieme,Scalable event-based clustering of social media via record linkagetechniques., in: ICWSM, 2011.
Costa, P.T., Jr., McCrae, R.R.: NEO PI-R Professional Manual. Psychological AssessmentResources, Odessa, FL (1992)
John, O., Srivastava, S.: The Big Five trait taxonomy: History, measurement, andtheoretical perspectives. In: Pervin, L., John, O. (eds.) Handbook of Personality: Theoryand Research, pp. 102-138. Guilford Press (1999)
John, O.P., Robins, R.W., Pervin, L.A.: Handbook of Personality: Theory and Research(3rd Edition). Guilford Press, New York (2008)
Rentfrow, P.J., Gosling, S.D.: The do re mi's of everyday life: the structure and personality correlate of music preferences. J PersSocPsychol 84, 1236-1256 (2003)
Kraaykamp, G., Eijck, K.: Personality, media preferences, and cultural participation.Personality and Individual Differences 38, 1675-1688 (2005)
Quercia, D., Lambiotte, R., Stillwell, D., Kosinski, M., Crowcroft, J.: The personality of popular facebook users. Proceedings of the ACM 2012 conference on ComputerSupported Cooperative Work, pp. 955-964. ACM, Seattle, Washington, USA (2012)
Ganzeboom, H.B.G.: Explaining differential participation in high-cultural activities: Aconfrontation of information-processing and status-seeking theories. In: Raub, W. (ed.)Theoretical models and empirical analyses: Contributions to the explanations of individual actions and collective phenomina. E.S. Publications, Utrecht (1982)
Zuckerman, M., Ulrich, R.S., McLaughlin, J.: Sensation seeking and reactions to naturepaintings. Personality and Individual Differences 15, 563-576 (1993)
Kosinski, M., Stillwell, D., Kohli, P., Bachrach, Y., Graepel, T.: Personality and WebsiteChoice. ACM Conference on Web Sciences, (2012)
Rosen, P., Kluemper, D.H.: The impact of the big five personality traits on the acceptanceof social networking website. AMCIS 2008 Proceedings 274 (2008)
Schrammel, J., Köffel, C., Tscheligi, M.: Personality traits, usage patterns and informationdisclosure in online communities. Proceedings of the 23rd British HCI Group AnnualConference on People and Computers: Celebrating People and Technology, pp. 169-174.British Computer Society, Cambridge, United Kingdom (2009)
Quercia, D., Kosinski, M., Stillwell, D., Crowcroft, J.: Our twitter profiles, our selves:Predicting personality with twitter. Privacy, security, risk and trust (passat), 2011 ieeethird international conference on and 2011 ieee third international conference on socialcomputing (socialcom), pp. 180-185 (2011)
Golbeck, J., Robles, C., Turner, K.: Predicting personality with social media. CHI '11Extended Abstracts on Human Factors in Computing Systems, pp. 253-262. ACM,Vancouver, BC, Canada (2011)
Bachrach, Y., Kosinski, M., Graepel, T., Kohli, P., Stillwell, D.: Personality and patterns of facebook usage. ACM Conference on Web Sciences, (2012)
Hughes, D.J., Rowe, M., Batey, M., Lee, A.: A tale of two sites: Twitter vs. Facebook and the personalitypredictors of social media usage. Comput. Hum. Behav. 28(2), 561–569 (2012)
Twitter API available on https://apps.Twitter.com/.
Cambria, B. Schuller, B. Liu, H. Wang & C. Havasi “Knowledge-based approaches to concept-level sentiment analysis”, IEEE Intelligent Systems, 28(2), pp.12–14, 2013.
E. Tupes and R. Christal, “Recurrent personality factors based on trait ratings”, Journal of Personality, vol. 60, no. 2,pp. 225–251, 1992.
R. McCrae and O. John, “An introduction to the five-factor model and its applications”, Journal of personality, vol. 60, no. 2, pp. 175–215, 1992.
J. Digman, “Personality structure: Emergence of the five-factor model”, Annual review of psychology, vol. 41, no. 1, pp. 417–440, 1990.
J. Han, M. Kamber, & J. Pei, “Data mining: concepts and techniques”, (3rded.), Morgan Kaufmann, 2011.
R. Wald, T. M. Khoshgoftaar, A. Napolitano, C. Sumner, “Using Twitter content to predict psychopathy”, in proceedings of the 2012 11th international conference on machine learning and applications—Volume 02, pp. 394–401, Washington, DC, USA.
R. Wald, T. Khoshgoftaar& C. Sumner, “Machine prediction of personality from Facebook profiles”, in 2012 IEEE 13th international conference on Information Reuse and Integration (IRI), pp. 109–115.
