Since an ever-increasing part of the population makes use of social media in their day-to-day lives, social media data is being analysed in many different disciplines. The social media analytics process involves four distinct steps, data discovery, collection, preparation, and analysis. While there is a great deal of literature on the challenges and difficulties involving specific data analysis methods, there hardly exists research on the stages of data discovery, collection, and preparation. To address this gap, we conducted an extended and structured literature analysis through which we identified challenges addressed and solutions proposed. The literature search revealed that the volume of data was most often cited as a challenge by researchers. In contrast, other categories have received less attention. Based on the results of the literature search, we discuss the most important challenges for researchers and present potential solutions. The findings are used to extend an existing framework on social media analytics. The article provides benefits for researchers and practitioners who wish to collect and analyse social media data.

Introduction

Social media has evolved over the last decade to become an important driver for acquiring and spreading information in different domains, such as business (Beier & Wagner, 2016), entertainment (Shen, Hock Chuan, & Cheng, 2016), science (Chen & Zhang, 2016), crisis management (Hiltz, Diaz, & Mark, 2011; Stieglitz, Bunker, Mirbabaie, & Ehnis, 2017a) and politics (Stieglitz & Dang-Xuan, 2013). One reason for the popularity of social media is the opportunity to receive or create and share public messages at low costs and ubiquitously. The enormous growth of social media usage has led to an increasing accumulation of data, which has been termed Social Media Big Data. Social media platforms offer many possibilities of data formats, including textual data, pictures, videos, sounds, and geolocations. Generally, this data can be divided into unstructured data and structured data (Baars & Kemper, 2008). In social networks, the textual content is an example of unstructured data, while the friend/follower relationship is an example of structured data. The growth of social media usage opens up new opportunities for analysing several aspects of, and patterns in communication. For example, social media data can be analysed to gain insights into issues, trends, influential actors and other kinds of information. Golder and Macy (2011) analysed Twitter data to study how people’s mood changes with time of day, weekday and season. In the field of Information Systems (IS), social media data is used to study questions such as the influence of network position on information diffusion (Susarla, Oh, & Tan, 2012). Many existing research papers are isolated case studies (Kim, Choi, & Natali, 2016; Li & Huang, 2014; Oh, Hu, & Yang, 2016) that collect a large data set during a specific time frame on a specific subject and analyse it quantitatively. Despite the variety of disciplines such projects can be found in, they have much in common. The steps necessary to gain useful information or even knowledge out of social media are often similar. Therefore, the field of “Social Media Analytics” aims to combine, extend, and adapt methods for the analysis of social media data (Stieglitz, Dang-Xuan, Bruns, & Neuberger, 2014). It has gained considerable attention and subsequently acceptance in academic research, but there is still a lack of comprehensive discussions of social media analytics, and of general models and approaches.