New paper: Classification and event identification using word embedding

A new paper “Classification and event identification using word embedding” is now available online.

This paper presents our contribution to the CLEF 2019 Protest-News Track, which aims to classify and identify protest events in English-language news from India and China. We used traditional classification models, namely, support vector machines and XGBoost classifiers, combined with various word embedding approaches. Multiple models were tested for experimental purposes, in addition to the two models evaluated within the official campaign. Results show promising performance, especially in terms of precision on both document and sentence classification tasks.

Twitter experiment at Royal Meteorological Society Conference

Michelle Spruce recently attended the Royal Meteorological Society (RMetS) Student & Early Career Researcher conference at the University of Birmingham on 4/5 July 2019.

As well as opening the conference by presenting her research on the social sensing of extreme weather events, Michelle also encouraged conference attendees to use Twitter during the conference in a social sensing experiment to understand the impact of ‘tweeting’ during an academic conference.

Over the 2 days of the conference attendees tweeted news and updates using the conference hashtag #RMetSStudents. By lunchtime on the second day of the conference with just 162 tweets Michelle was able to demonstrate the wider impact of these tweets:

By the end of the conference, 203 tweets including this hashtag were generated, from 44 users in 6 countries and 13 cities. While a seemingly small amount of data, by the end of the conference these tweets generated a potential reach of over 32,000 Twitter users and over 500,000 impressions (individual views of these tweets). This simple experiment demonstrated the power of using Twitter as a source of information even for small scale events such as this.