Media Perception Analytics Based on News Reports of 2013 Boston Marathon Bombing

Experiencing mass violence, terrorism, or other traumatic events can shape how individuals perceive and respond to their social world. Anecdotally, following extensive media coverage of mass violence events, many report perceiving objects, people, and situations as particularly threatening; and, as media coverage shifts to emphasize resilience and community cohesion, threats seems to dissipate.

 

This project, based on 2013 Boston Marathon Bombing, test how emotionally potent media coverage of a real-world threat alters threat perception. This work could reveal potential harmful real-world consequences of emotionally potent media reporting of a terrorism event as well as suggest methods for alleviating such effects (e.g., by reporting on positive responses to such tragedies, like the heroics of first responders). This work will also help characterize the types of individuals who are at greatest risk of altered threat perception after a mass violence or terrorism event or when media attention to such events increases. 

MOTIVATION

Project primarily funded by NSF (National Science Foundation)

ACHIEVEMENT FLOW

Surveyed and determined news ​outlets by results of questionnaire 

Compared and determined news sources

 

Collected news article URLs using google news by Python

Retrieved and parsed news article content using jsoup

 

Count sentiment words from the news article content using LIWC

Created outlet measures

PROCESS

OUTLET AND WAVE DETERMINED

Based on the survey result, we focus on 4 main outlets:

  • BG: Boston Globe

  • BH: Boston Herald

  • NT: New York Times

  • MT: Boston Metro

And the news collection periods correspond to the participants’ lab experiment.

  • Wave 1: 01/24/2014-03/15/2014 (50 days)

  • Wave 2: 03/14/2014-04/22/2014 (40 days)

  • Wave 3: 05/27/2014-11/24/2014 (182 days)

COLLETED NEWS ARTICLE URLS USING GOOGLE NEWS

RETRIEVED AND PARSED NEWS ARTICLE CONTENT USING JSOUP

RETRIEVED AND PARSED NEWS ARTICLE CONTENT USING JSOUP