Collecting event‐related tweets from twitter stream

Abstract

Twitter provides a channel of collecting and publishing instant information on major events like natural disasters. However, information flow on Twitter is of great volume. For a specific event, messages collected from the Twitter Stream based on either location constraint or predefined keywords would contain a lot of noise. In this article, we propose a method to achieve both high‐precision and high‐recall in collecting event‐related tweets. Our method involves an automatic keyword generation component, and an event‐related tweet identification component. For keyword generation, we consider three properties of candidate keywords, namely relevance, coverage, and evolvement. The keyword updating mechanism enables our method to track the main topics of tweets along event development. To minimize annotation effort in identifying event‐related tweets, we adopt active learning and incorporate multiple‐instance learning which assigns labels to bags instead of instances (that is, individual tweets). Through experiments on two real‐world events, we demonstrate the superiority of our method against state‐of‐the‐art alternatives.

Visualization of Emergency Needs Posted on Social Media by Metaphor Map

“…Using distributed learning can achieve good results in identifying informative posts from a large number of noninformative posts (Ghafarian & Yazdi, 2020). An automatic keyword generation mechanism can also be used to enable managers to track event topics more accurately along with events in data collection (Zheng & Sun, 2019). Social media data can also be combined with the content, sentiment, and social network analysis to help emergency responders plan rescue work and find people in need of medical and emotional assistance (Kiatpanont, Tanlamai, & Chongstitvatana, 2016;Wu & Cui, 2018).…”

Can we detect trends in natural disaster management with artificial intelligence? A review of modeling practices

“…For example, Chou et al (2014) developed an ontology-based evaluation tool to evaluate the utility of NDM websites. Zheng et al (2019) proposed a method to achieve both high-precision and high-recall in collecting event-related tweets based on active learning and multiple-instance learning. Emergency resource demand prediction.…”

A Comparative Study of Keywords and Sentiments of Abstracts by Python Programs

“…The previous studies on keywords generation can be traced in these scholars' works (Joshi & Motwani, 2006;Thomaidou & Vazirgiannis, 2011;Hussey et al, 2012;Liu et al, 2014;Savva et al, 2014;Scholz, et al, 2019;Arora & Kumar, 2019;Zheng & Sun, 2019;Thushara et al, 2019). Among the works by these researchers, Scholz, et al (2019) propose an automated approach for generating keywords for Sponsored Search Advertising based on his keyword generation algorithms.…”

“…Among the works by these researchers, Scholz, et al (2019) propose an automated approach for generating keywords for Sponsored Search Advertising based on his keyword generation algorithms. Zheng & Sun (2019) utilize the three properties of relevance, coverage, and evolvement of candidate keywords by using active learning and multiple-instance learning to follow up the main topics of tweets along the development of events. Few researchers have assessed the reliability of the keyword generated by different Python programs.…”