Extracting and Quantifying Actionable Knowledge Using Twitter Data
Bhavana Ramesh, Charles M. Weber · 2022 Portland International Conference on Management of Engineering and Technology (PICMET) · 2022
Knowledge management is essential for businesses to gain competitive advantage. Social platforms like Twitter, Facebook, and others hold important information external to the organization. These platforms are enablers to creating, sharing and forming opinions about topics of interest. External knowledge management of social data possess two types of challenges to knowledge workers and managers: 1) unfamiliarity of methods to obtain data and distill actionable knowledge; and 2) inability to quantify the acquired knowledge. Without familiarity in methods and quantifiable knowledge, justifying return on investments on the knowledge-related programs is inefficient. This research studies existing methods-topic mining and sentiment analysis-to obtain actionable knowledge, and it formulates a novel framework to mine external knowledge from Twitter. It also identifies the nature of actionable knowledge metrics that can be applied to a broad knowledge discovery process. The framework is applied on a dataset and the results are discussed. This study benefits knowledge workers and managers by identifying and quantifying actionable knowledge from social platforms. Thus, it provides ROI justifications and contribute toward achieving measurable knowledge value for businesses.