Initial Exploration on an Effective Social Media Analytics Method and Algorithm for Instagram Hashtags

Initial Exploration on an Effective Social Media Analytics Method and Algorithm for Instagram Hashtags

Nurul Atikah Ahmad Rosli (Universiti Sains Malaysia, Penang, Malaysia) and Mohd Heikal Husin (Universiti Sains Malaysia, Penang, Malaysia)
Copyright: © 2019 |Pages: 15
DOI: 10.4018/IJEBR.2019070101

Abstract

Over the years, social media has brought many benefits to different fields, especially in the business sector. Most of the existing organizations have taken these benefits to actively engage with the public to increase their online business value. The use of hashtags on numerous social media platforms especially on Instagram is one of the highly used benefits. By tagging specific postings, business organizations are able to promote and communicate with their customers directly in a more interactive manner. In this article, the authors are exploring the following: (1) to determine the effectiveness of the existing analytics method (text identification and trend analysis) for analyzing Instagram hashtag data and; (2) to determine the effectiveness of existing analytic techniques such as Naïve Bayes and Support Vector Machines (SVM) suited for the selected analytics method. As a result, the authors have identified that the combination of Trend Analysis method and SVM are an effective social media analytics approach for analyzing Instagram hashtag data.
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Introduction

In recent years, social media have been used by millions of users such as Facebook for social networking, Twitter for micro-blogging and Instagram for photo and video sharing activities. Fundamentally, these social media sites rely on their users to create and contribute content, to annotate others’ content with tags and comments and to form online relationships (Guy et al., 2012). By referring to the general social media statistics in 2016, there are 3 main platforms that have been highlighted consistently (Parker, 2016). Facebook has over 1.79 billion monthly active users, which represents a 17 percent increase over the years. Twitter, on the other hand, has about 317 million monthly active users that send around 6,000 tweets per second, which corresponds to over 350,000 tweets sent per minute and 500 million tweets per day. Meanwhile, Instagram has over 500 million monthly active users share an average of 95 million photos and videos per day. It is common to find the pound symbol or hash symbol (#) or ‘hashtag’ used before a word on these platforms. Hashtag allows web search engines to find and categorize messages, keywords, picture, and to annotate other users. In August 2007, hashtag is first proposed for use on Twitter, followed by Instagram which added the hashtag support in January 2011 whereas, Facebook only begun supporting hashtag in June 2013. Most existing research has been focusing on the use of these components on Twitter and Facebook without much exploration on the Instagram platform (Batrinca et al., 2015; Gorvankolla et al., 2017). As such, the authors are focusing on Instagram in order to explore its usage of hashtag in this paper.

Since its introduction, the hashtag has become an essential component among users and have been frequently used in the business events or promotions since it supports high visibility online. An example of this can be seen from Instagram, where 48.8% of business brands have utilized Instagram as a marketing tool to promote their products and connect with their customers (Parker, 2016; Mittal et al., 2017). In Instagram, the use of hashtag especially in business is very effective when used for purposes of branding. For a business organization that utilizes social media platform such as Instagram, the use of hashtag in a post caption is done to increase the level of social media engagement. In this research, the authors aim to apply similar social media analytics method applied in Facebook and Twitter to determine the effectiveness of the methods for Instagram data on the use of hashtag. Instagram exhibits a mixture of features including social structure, social tagging, and media sharing (Ferrara et al., 2014). Users could upload, “like”, comment, and repost the relevant posts.

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