Abstract: This session will cover data generated on social media and how it is consumed. This data is used for data science applications in a variety of different ways. The platforms themselves use it for data intelligence, but also independent brands use social media data for several applications. Furthermore, content creators, researchers, and businesses across many verticals apply Machine Learning and Deep Learning on social media data for a variety of purposes. Data analytics, marketing campaigns, stock price predictions, recommendation systems, mental health indicators, and chatbots are some examples of the data science applications.
Given that social media is such a large part of our everyday lives today and it is highly influenced by data science algorithms, several layers of historical and sectional biases have inevitably made their way into the models and incidentally into our everyday lives. Biases and challenges will be discussed along with efforts underway for a better informed future.
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