Topy: Real-Time Story Tracking via Social Tags
Abstract
The Topy system automates real-time story tracking by utilizing crowd-sourced tagging on social media platforms. Topy employs a state-of-the-art Twitter hashtag recommender to continuously annotate news articles with hashtags, a rich meta-data source that allows connecting articles under drastically different timelines than typical keyword based story tracking systems. Employing social tags for story tracking has the following advantages: (1) social annotation of news enables the detection of emerging concepts and topic drift in a story; (2) hashtags go beyond topics by grouping articles based on connected themes (e.g., #rip, #blacklivesmatter, #icantbreath); (3) hashtags link articles that focus on subplots of the same story (e.g., #palmyra, #isis, #refugeecrisis).
Cite
Text
Poghosyan et al. "Topy: Real-Time Story Tracking via Social Tags." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016. doi:10.1007/978-3-319-46131-1_10Markdown
[Poghosyan et al. "Topy: Real-Time Story Tracking via Social Tags." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016.](https://mlanthology.org/ecmlpkdd/2016/poghosyan2016ecmlpkdd-topy/) doi:10.1007/978-3-319-46131-1_10BibTeX
@inproceedings{poghosyan2016ecmlpkdd-topy,
title = {{Topy: Real-Time Story Tracking via Social Tags}},
author = {Poghosyan, Gevorg and Qureshi, Muhammad Atif and Ifrim, Georgiana},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2016},
pages = {45-49},
doi = {10.1007/978-3-319-46131-1_10},
url = {https://mlanthology.org/ecmlpkdd/2016/poghosyan2016ecmlpkdd-topy/}
}