CrisisLex: A Lexicon for Collecting and Filtering Microblogged Communications in Crises

Locating timely, useful information during crises and mass emergencies is critical for those forced to make potentially life-altering decisions. As the use of Twitter to broadcast useful information during such situations becomes more widespread, the problem of finding it becomes more difficult. We describe an approach toward improving the recall in the sampling of Twitter communications that can lead to greater situational awareness during crisis situations. First, we create a lexicon of crisis-related terms that frequently appear in relevant messages posted during different types of crisis situations. Next, we demonstrate how we use the lexicon to automatically identify new terms that describe a given crisis. Finally, we explain how to efficiently query Twitter to extract crisis-related messages during emergency events. In our experiments, using a crisis lexicon leads to substantial improvements in terms of recall when added to a set of crisis-specific keywords manually chosen by experts; it also helps to preserve the original distribution of message types

From 2,772 segments to five personas: Summarizing a diverse online audience by generating culturally adapted personas

Understanding users in the era of social media is challenging, requiring organizations to adopt novel computation-aided approaches. To exemplify such an approach, we retrieved information on millions of interactions with YouTube video content from a major Middle Eastern media outlet, to automatically generate personas that capture how different audience segments interact with thousands of individual content pieces. Then, we used qualitative data to provide additional insights into the automatically generated persona profiles. Our findings provide insights into social media usage in the Middle East and demonstrate the application of a novel methodology that generates culturally adapted personas of social media audiences, summarizing complex social analytics data into human portrayals that are easy to understand by end users in real organizations.</p

Understanding the Use of Crisis Informatics Technology among Older Adults

Mass emergencies increasingly pose significant threats to human life, with a disproportionate burden being incurred by older adults. Research has explored how mobile technology can mitigate the effects of mass emergencies. However, less work has examined how mobile technologies support older adults during emergencies, considering their unique needs. To address this research gap, we interviewed 16 older adults who had recent experience with an emergency evacuation to understand the perceived value of using mobile technology during emergencies. We found that there was a lack of awareness and engagement with existing crisis apps. Our findings characterize the ways in which our participants did and did not feel crisis informatics tools address human values, including basic needs and esteem needs. We contribute an understanding of how older adults used mobile technology during emergencies and their perspectives on how well such tools address human values.Comment: 10 page

EMTerms 1.0: A Terminological Resource for Crisis Tweets

ABSTRACT We present the first release of EMTerms (Emergency Management Terms), the largest crisis-related terminological resource to date, containing over 7,000 terms used in Twitter to describe various crises. This resource can be used by practitioners to search for relevant messages in Twitter during crises, and by computer scientists to develop new automatic methods for crises in Twitter. The terms have been collected from a seed set of terms manually annotated by a linguist and an emergency manager from tweets broadcast during 4 crisis events. A Conditional Random Fields (CRF) method was then applied to tweets from 35 crisis events, in order to expand the set of terms while overcoming the difficulty of getting more emergency managers&apos; annotations. The terms are classified into 23 information-specific categories, by using a combination of expert annotations and crowdsourcing. This article presents the detailed terminology extraction methodology, as well as final results