Extracting News Events from Microblogs

TL;DR

This paper presents a real-time method for detecting news events from Twitter streams using deep learning, clustering, and ranking, achieving state-of-the-art performance on large annotated datasets.

Contribution

It introduces a novel streaming clustering algorithm and an integrated approach combining deep learning and ranking for real-time news event detection from Twitter.

Findings

Achieves state-of-the-art detection accuracy

Effectively clusters news-related tweets in real-time

Outperforms existing solutions in evaluation

Abstract

Twitter stream has become a large source of information for many people, but the magnitude of tweets and the noisy nature of its content have made harvesting the knowledge from Twitter a challenging task for researchers for a long time. Aiming at overcoming some of the main challenges of extracting the hidden information from tweet streams, this work proposes a new approach for real-time detection of news events from the Twitter stream. We divide our approach into three steps. The first step is to use a neural network or deep learning to detect news-relevant tweets from the stream. The second step is to apply a novel streaming data clustering algorithm to the detected news tweets to form news events. The third and final step is to rank the detected events based on the size of the event clusters and growth speed of the tweet frequencies. We evaluate the proposed system on a large, publicly available corpus of annotated news events from Twitter. As part of the evaluation, we compare our approach with a related state-of-the-art solution. Overall, our experiments and user-based evaluation show that our approach on detecting current (real) news events delivers a state-of-the-art performance.