Identifying Time-varying Drivers of Social Media Issues and Conversations

Abstract

Successfully understanding of social media conversation growth, dissemination and extinction is a challenging task that relies on identifying groups, group influence, diffusion models, forecast models, social dynamics and text analytics. In this problem, we concentrate on the description of a novel approach for identifying drivers of the direction and momentum of social conversations, including the spread of mood, sentiment and issues. The approach first groups potential drivers of conversation based on variability. The primary driver in each group is then selected. Finally, the relationship between the selected drivers and the topic outcome is calculated and displayed visually. This enables the quick identification of the form and structure of the conversation and allows us to predict momentum, direction, contagion risks, potential responses and interventions. There is a huge amount of data in the form of text available today in the internet across various channels – social media, news articles, blogs, e-commerce websites. Most of this data is a part of some “conversation” or the other where real-world entities discuss, analyze, comment, exchange information in the form of written expressions in textual format. Driver Modeling on textual data can be useful in observing the key drivers which are driving the “conversation” coupled with the associated sentiments and mood states for the observed key drivers. These insights about the key conversational drivers are often used in a variety of domains such as tracking news cycles, stock movements, legislation developments, brand image, viral breakouts and much more.