Information quality challenges in social media

Social media has become an integral part of people’s lives. People share their daily activities, experiences, interests, and opinions on social networking websites, opening the floodgates of information that can be analyzed by marketers as well as consumers. However, low barriers to publication and easy-to-use interactive interfaces have contributed to various information quality (IQ) problems in the social media that has made obtaining timely, accurate and relevant information a challenge. Approaches such as data mining and machine learning have only begun to address these challenges. Social media has its own distinct characteristics that warrant specialized approaches. In this paper, we study the unique characteristics of social media and address how existing methods fall short in mitigating the IQ issues it faces. Despite being extensively studied, IQ theories have yet to be embraced in tackling IQ challenges in social media. We redefine social media challenges as IQ challenges. We propose an IQ and Total Data Quality Management (TDQM) approach to the Social media challenges. We map the IQ dimensions, social media categories, social media challenges, and IQ tools in order to bridge the gap between the IQ framework and its application in addressing IQ challenges in social media.