Conference paper Open Access

Attend2trend: Attention Model for Real-Time Detecting and Forecasting of Trending Topics

Ahmed Saleh; Ansgar Scherp

Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Ahmed Saleh</dc:creator>
  <dc:creator>Ansgar Scherp</dc:creator>
  <dc:description>Knowing what is increasing in popularity is important to researchers, news organizations, auditors, government entities and more. In particular, knowledge of trending topics provides us with information about what people are attracted to and what they think is noteworthy. Yet detecting trending topics from a set of texts is a difficult task, requiring detectors to learn trending patterns  while simultaneously making predictions.

In this paper, we propose a deep learning model architecture for the challenging task of trend detection and forecasting. The model architecture aims to learn and attend to the trending values' patterns. Our preliminary results show that our model detects the trending topics with a high accuracy. </dc:description>
  <dc:description>This is the author's version of the work. It is posted here for your personal use, not for redistribution. The definitive Version of Record was published in the proceedings of the IEEE International Conference on Data Mining Workshops (ICDMW),</dc:description>
  <dc:subject>Deep learning</dc:subject>
  <dc:title>Attend2trend: Attention Model for Real-Time Detecting and Forecasting of Trending Topics</dc:title>
Views 91
Downloads 128
Data volume 20.5 MB
Unique views 71
Unique downloads 122


Cite as