Intelligent Disaster Response via Social Media Analysis A Survey <s> Lexicon Unification and Extension <s> In this paper, we propose a low-tech solution for use by microbloggers that could enhance their ability to rapidly produce parsable, crisis-relevant information in mass emergencies. We build upon existing research on the use of social media during mass emergencies and disasters. Our proposed intervention aims to leverage the affordances of mobile microblogging and the drive to support citizen reporting within current behavioral Twitter-based microblogging practice. We introduce a prescriptive, tweet-based syntax that could increase the utility of information generated during emergencies by gently reshaping current behavioral practice. This offering is grounded in an understanding of current trends in norm evolution of Twitter use, an evolution that has progressed quickly but appears to be stabilizing around specific textual conventions. <s> BIB001 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Lexicon Unification and Extension <s> Keywords: Crisis Informatics ; Social Media Collection ; Social Media Analysis Reference EPFL-CONF-203561 Record created on 2014-11-26, modified on 2017-05-12 <s> BIB002 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Lexicon Unification and Extension <s> 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’ 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. <s> BIB003
One way of increasing the visibility of disaster-related tweets is extending expert-defined keyword sets. For example, "CrisisLex" BIB002 is a a lexicon that increases the portion of disaster-related tweets that are captured from the Twitter Streaming API. The effort is towards finding one set of keywords which are extensively used in different disasters (hurricane, tornado, flood, bombing, and explosion). The process starts with extracting the tweets that contain any word from a set of expert-suggested keywords. These tweets are manually labeled to remove the ones which are not releated to the disaster. From the crisis-related tweets, words and phrases (consisting of two words) that appear in at least 5% of tweets form CirisLex. Another lexicon is "EMTerms 1.0"(CrisisLex and EMTerms 1.0 can be obtained from http://crisislex.org/crisis-lexicon.html) that includes more than 7,000 words categorized into 23 groups was introduced. Their method starts with the keywords of four major events and then extend the lexicon using Conditional Random Field (CRF) on another 35 disasters BIB003 . Another way is providing instructions for users on how to tweet regarding a disaster. Microsyntaxes, the instructions, unify the format of posts and make it possible for machines to automatically extract all the posts on a specific issue. "Tweak the tweet" BIB001 is a microsyntax introduced after Red River Floods, 2009. The authors show that visibility of disaster-related tweets increases when users are instructed to use hashtags such as #fargo, #redriver, and #flood09.