Empirical Study on Detecting Controversy in Social Media
This work addresses the challenge for companies and investors in evaluating social consciousness due to lack of systematic data, but it is incremental as it applies existing clustering and sentiment analysis methods to a new domain.
The paper tackles the problem of detecting controversial events in social media to assess corporate social consciousness, introducing a system that uses Twitter data to identify events and shows their impact on market volatility, with a case study on the Starbucks Philadelphia arrests demonstrating functionality.
Companies and financial investors are paying increasing attention to social consciousness in developing their corporate strategies and making investment decisions to support a sustainable economy for the future. Public discussion on incidents and events -- controversies -- of companies can provide valuable insights on how well the company operates with regards to social consciousness and indicate the company's overall operational capability. However, there are challenges in evaluating the degree of a company's social consciousness and environmental sustainability due to the lack of systematic data. We introduce a system that utilizes Twitter data to detect and monitor controversial events and show their impact on market volatility. In our study, controversial events are identified from clustered tweets that share the same 5W terms and sentiment polarities of these clusters. Credible news links inside the event tweets are used to validate the truth of the event. A case study on the Starbucks Philadelphia arrests shows that this method can provide the desired functionality.