Conference paper Open Access

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

Ahmed Saleh; Ansgar Scherp


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{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<pre>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.</pre>\n\n<pre>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&#39; patterns. Our preliminary results show that our model detects the trending topics with a high accuracy. </pre>", 
  "license": "http://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "ZBW - Leibniz Information Centre for Economics", 
      "@type": "Person", 
      "name": "Ahmed Saleh"
    }, 
    {
      "affiliation": "University of Stirling", 
      "@type": "Person", 
      "name": "Ansgar Scherp"
    }
  ], 
  "headline": "Attend2trend: Attention Model for Real-Time Detecting and Forecasting of Trending Topics", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2018-11-17", 
  "url": "https://zenodo.org/record/2600798", 
  "keywords": [
    "Deep learning"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.1109/ICDMW.2018.00222", 
  "@id": "https://doi.org/10.1109/ICDMW.2018.00222", 
  "@type": "ScholarlyArticle", 
  "name": "Attend2trend: Attention Model for Real-Time Detecting and Forecasting of Trending Topics"
}
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