• Skip to main content
  • Skip to primary sidebar
AAAI

AAAI

Association for the Advancement of Artificial Intelligence

    • AAAI

      AAAI

      Association for the Advancement of Artificial Intelligence

  • About AAAIAbout AAAI
    • AAAI Officers and Committees
    • AAAI Staff
    • Bylaws of AAAI
    • AAAI Awards
      • Fellows Program
      • Classic Paper Award
      • Dissertation Award
      • Distinguished Service Award
      • Allen Newell Award
      • Outstanding Paper Award
      • Award for Artificial Intelligence for the Benefit of Humanity
      • Feigenbaum Prize
      • Patrick Henry Winston Outstanding Educator Award
      • Engelmore Award
      • AAAI ISEF Awards
      • Senior Member Status
      • Conference Awards
    • AAAI Resources
    • AAAI Mailing Lists
    • Past AAAI Presidential Addresses
    • Presidential Panel on Long-Term AI Futures
    • Past AAAI Policy Reports
      • A Report to ARPA on Twenty-First Century Intelligent Systems
      • The Role of Intelligent Systems in the National Information Infrastructure
    • AAAI Logos
    • News
  • aaai-icon_ethics-diversity-line-yellowEthics & Diversity
  • Conference talk bubbleConferences & Symposia
    • AAAI Conference
    • AIES AAAI/ACM
    • AIIDE
    • IAAI
    • ICWSM
    • HCOMP
    • Spring Symposia
    • Summer Symposia
    • Fall Symposia
    • Code of Conduct for Conferences and Events
  • PublicationsPublications
    • AAAI Press
    • AI Magazine
    • Conference Proceedings
    • AAAI Publication Policies & Guidelines
    • Request to Reproduce Copyrighted Materials
  • aaai-icon_ai-magazine-line-yellowAI Magazine
    • Issues and Articles
    • Author Guidelines
    • Editorial Focus
  • MembershipMembership
    • Member Login
    • Developing Country List
    • AAAI Chapter Program

  • Career CenterCareer Center
  • aaai-icon_ai-topics-line-yellowAITopics
  • aaai-icon_contact-line-yellowContact

Home / Proceedings / Proceedings of the International AAAI Conference on Web and Social Media

Characterizing Collective Attention via Descriptor Context: A Case Study of Public Discussions of Crisis Events

February 1, 2023

Download PDF

Authors

Ian Stewart,Diyi Yang,Jacob Eisenstein

Georgia Institute of Technology,Georgia Institute of Technology,Georgia Institute of Technology


DOI:

10.1609/icwsm.v14i1.7331


Abstract:

Social media datasets make it possible to rapidly quantify collective attention to emerging topics and breaking news, such as crisis events. Collective attention is typically measured by aggregate counts, such as the number of posts that mention a name or hashtag. But according to rationalist models of natural language communication, the collective salience of each entity will be expressed not only in how often it is mentioned, but in the form that those mentions take. This is because natural language communication is premised on (and customized to) the expectations that speakers and writers have about how their messages will be interpreted by the intended audience. We test this idea by conducting a large-scale analysis of public online discussions of breaking news events on Facebook and Twitter, focusing on five recent crisis events. We examine how people refer to locations, focusing specifically on contextual descriptors, such as “San Juan” versus “San Juan, Puerto Rico.” Rationalist accounts of natural language communication predict that such descriptors will be unnecessary (and therefore omitted) when the named entity is expected to have high prior salience to the reader. We find that the use of contextual descriptors is indeed associated with proxies for social and informational expectations, including macro-level factors like the location's global salience and micro-level factors like audience engagement. We also find a consistent decrease in descriptor context use over the lifespan of each crisis event. These findings provide evidence about how social media users communicate with their audiences, and point towards more fine-grained models of collective attention that may help researchers and crisis response organizations to better understand public perception of unfolding crisis events.

Topics: ICWSM

Primary Sidebar

HOW TO CITE:

Ian Stewart,Diyi Yang,Jacob Eisenstein Characterizing Collective Attention via Descriptor Context: A Case Study of Public Discussions of Crisis Events Proceedings of the International AAAI Conference on Web and Social Media (2020) 650-660.

Ian Stewart,Diyi Yang,Jacob Eisenstein Characterizing Collective Attention via Descriptor Context: A Case Study of Public Discussions of Crisis Events ICWSM 2020, 650-660.

Ian Stewart,Diyi Yang,Jacob Eisenstein (2020). Characterizing Collective Attention via Descriptor Context: A Case Study of Public Discussions of Crisis Events. Proceedings of the International AAAI Conference on Web and Social Media, 650-660.

Ian Stewart,Diyi Yang,Jacob Eisenstein. Characterizing Collective Attention via Descriptor Context: A Case Study of Public Discussions of Crisis Events. Proceedings of the International AAAI Conference on Web and Social Media 2020 p.650-660.

Ian Stewart,Diyi Yang,Jacob Eisenstein. 2020. Characterizing Collective Attention via Descriptor Context: A Case Study of Public Discussions of Crisis Events. "Proceedings of the International AAAI Conference on Web and Social Media". 650-660.

Ian Stewart,Diyi Yang,Jacob Eisenstein. (2020) "Characterizing Collective Attention via Descriptor Context: A Case Study of Public Discussions of Crisis Events", Proceedings of the International AAAI Conference on Web and Social Media, p.650-660

Ian Stewart,Diyi Yang,Jacob Eisenstein, "Characterizing Collective Attention via Descriptor Context: A Case Study of Public Discussions of Crisis Events", ICWSM, p.650-660, 2020.

Ian Stewart,Diyi Yang,Jacob Eisenstein. "Characterizing Collective Attention via Descriptor Context: A Case Study of Public Discussions of Crisis Events". Proceedings of the International AAAI Conference on Web and Social Media, 2020, p.650-660.

Ian Stewart,Diyi Yang,Jacob Eisenstein. "Characterizing Collective Attention via Descriptor Context: A Case Study of Public Discussions of Crisis Events". Proceedings of the International AAAI Conference on Web and Social Media, (2020): 650-660.

Ian Stewart,Diyi Yang,Jacob Eisenstein. Characterizing Collective Attention via Descriptor Context: A Case Study of Public Discussions of Crisis Events. ICWSM[Internet]. 2020[cited 2023]; 650-660.


ISSN: 2334-0770


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. By clicking “Accept All”, you consent to the use of ALL the cookies. However, you may visit "Cookie Settings" to provide a controlled consent.
Cookie SettingsAccept All
Manage consent

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary
Always Enabled
Necessary cookies are absolutely essential for the website to function properly. These cookies ensure basic functionalities and security features of the website, anonymously.
CookieDurationDescription
cookielawinfo-checkbox-analytics11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".
cookielawinfo-checkbox-functional11 monthsThe cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
cookielawinfo-checkbox-necessary11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
cookielawinfo-checkbox-others11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.
cookielawinfo-checkbox-performance11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".
viewed_cookie_policy11 monthsThe cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.
Functional
Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features.
Performance
Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.
Analytics
Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc.
Advertisement
Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. These cookies track visitors across websites and collect information to provide customized ads.
Others
Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet.
SAVE & ACCEPT