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2017 EACL EACL 2017

Audience Segmentation in Social Media

Abstract

AbstractUnderstanding the social media audience is becoming increasingly important for social media analysis. This paper presents an approach that detects various audience attributes, including author location, demographics, behavior and interests. It works both for a variety of social media sources and for multiple languages. The approach has been implemented within IBM Watson Analytics for Social Media and creates author profiles for more than 300 different analysis domains every day.

🌉 Interdisciplinary Bridge — Data Science & Analytics and Interdisciplinary and Machine Learning
🧭 Keyword Pioneer — user profiling
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio

Authors

Verena Henrich , Alexander Lang

Topics

Machine Learning > Core Methods > Classification Machine Learning > Core Methods > Clustering Data Science & Analytics > Applications > Clustering Interdisciplinary > Social > Social Media Analysis

Keywords

text classification social media analysis user profiling demographic analysis author profiling audience segmentation interest detection
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