Insights from Twitter Analytics: Modeling Social Media Personality Dimensions and Impact of Breakthrough Events
Social media and big data have been in high focus due to their potentially huge impact on business, society and polity. This research contributes to the same domain and peruses the twitter community before and after an event which is a major breakthrough
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Abstract. Social media and big data have been in high focus due to their potentially huge impact on business, society and polity. This research contributes to the same domain and peruses the twitter community before and after an event which is a major breakthrough for an economy. Here, the event being monitored is the Union Budget-2016 in India. The research taps the occasion to understand the various groups which participate in the online discussion amongst 43,924 tweets from 22,896 users and the pre and post budget twitter metrics are analyzed, deducing the sensitivity of the groups to the day of proposal of the budget. The research framework incorporates twitter analytics and relies on visual and quantitative data, drawing inferences from the intelligence. How the personality dimensions change before and after the event, is also analyzed. This change in dimensions can directly account for the influencing nature of the social media group. Keywords: Twitter analytics Social media Big data Network analytics Content analytics Sentiment analytics Brand personality Dimensions
1 Introduction The emergence of big data has created a new awakening in the business and research community. Such a large pool of data, being incremented every single second presents tremendous opportunity to analyze and reasonably pre-empt certain trends with a fair amount of certainty. Social Media [1, 17], out of all has been one of the main sources of generating data and has drawn interest of businesses for marketing purposes, which includes product development, service promotion, customer engagement as well as brand promotion. Research groups from diverse areas have been involved in observing and utilizing the opportunities presented by social media analytics to a great effect. Significant amount of work has been done in collecting and processing data through various portals to gain insights into several areas such as stock price prediction, Relief measures, Crisis Management, early event prediction, election prediction, public relations and public opinion [2, 3]. However, the analysis of the data presents its challenges due to its high variety, veracity, volume and velocity with which it is created and generated. © IFIP International Federation for Information Processing 2016 Published by Springer International Publishing Switzerland 2016. All Rights Reserved Y.K. Dwivedi et al. (Eds.): I3E 2016, LNCS 9844, pp. 533–544, 2016. DOI: 10.1007/978-3-319-45234-0_47
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A. Lakhiwal and A.K. Kar
Twitter [38–40], in particular, has been actively leveraged in facilitating social media analytics. Users active on twitter and interested in certain topics can easily communicate with one another using rapid and ad hoc establishment of shared ‘hashtags’ [33–35] which integrates the users even if they are not following each other. These hashtags along with the other metadata forms a basis for data extraction and analysis. Due to its open [3] architecture, twitter allows researchers to integrate smoothly to its API [4] and search for the d
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