Tapping into Social Media in Crisis: A Survey (2025)

Abstract: When a crisis hits, people often turn to social media to ask for help, offer help, find out how others are doing, and decide what they should do. The growth of social media use during crises has been helpful to aid providers as well, giving them a nearly immediate read of the on-the-ground situation that they might not otherwise have. The amount of crisis-related content posted to social media over the past two decades has been explosive, which, in turn, has been a boon to Language Technology (LT) researchers. In this study, we conducted a systematic survey of 355 papers published in the past five years to better understand the expanding growth of LT as it is applied to crisis content, specifically focusing on corpora built over crisis social media data as well as systems and applications that have been developed on this content. We highlight the challenges and possible future directions of research in this space. Our goal is to engender interest in the LT field writ large, in particular in an area of study that can have dramatic impacts on people’s lives. Indeed, the use of LT in crisis response has already been shown to save people’s lives.

## introduction: language technologies and crises the aftermath of the haitian earthquake of 2010 saw the development and deployment of language technologies at a large and national scale for the first-time ever in a crisis. most notably, language technologies were developed for a language that most in the nlp field had never heard of, and likewise most aid providers did not speak, namely, haitian kreyòl. at its peak, in the hours and days after the earthquake, first-responders in haiti were receiving over 5,000 sms messages per hour asking for help, over 80% of which were in kreyòl. in response to the desperate need, a diverse group of individuals, notably driven by the haitians themselves, developed and deployed technologies that could process this load, with a heavy reliance on crowdsourcing, the latter of which tapped into haiti's large world-wide diaspora. although the language technologies developed at the time are archaic by today's standards, these technologies allowed for the rapid triaging of the sms messages @xcite , geolocation (mostly through crowdsourcing) @xcite , and even machine translation @xcite . the infrastructure and language technologies developed for this crisis were credited with saving thousands of lives @xcite . the haitian earthquake, and the crisis it caused, are not unique. in fact, natural or human-caused crises happen regularly around the globe. populations tend to use social media (and sms) to report on how they are being affected. the data posted to social media have proven essential for providing and directing aid. further, in notable examples and ongoing research, language technologies have proven, or can be shown, to be essential tools in the crisis preparedness and response toolkit. ## background and related work there are a host of issues one must contend with when harvesting and processing data from social media platforms as relates to crises, much of which relies on language technologies: identifying the language and using language-specific tools for text or audio in a language (or relevant multilingual models); identifying named entities of various types within a text; identifying location information, including fine-grained mentions; extracting timeline information to provide a step-by-step view of a crisis as it unfolds; analyzing the sentiment or stance of affected populations; determining whether messages are relevant to the crisis at hand, and if so, what urgency they represent (i.e., triage); filtering out irrelevant content, such as misinformation or spam, or even disinformation; and, producing a summary of ongoing events for aid providers or government bodies (i.e., a situation report, or sitrep). all of the above rely on, or would benefit significantly from, the use of language technologies. crucially, given the millions of users on social media platforms, information can be harvested to identify the need on the ground, summarize the extent of a disaster locally, and also direct aid. the birth of the multidisciplinary field of crisis informatics @xcite @xcite saw the first forays into the use of language technologies in crisis response, focused primarily on disaster warning, response and recovery. a notable (and likely first) example of social media use in crisis was on twitter, where users reported localized information regarding the san diego firestorm of 2007 @xcite . however, it was not until haiti in 2010 that the use of technologies for identifying and meeting local need demonstrated the potential for language technological solutions (albeit across sms messages, not social media directly) @xcite . in the uk floods of 2012 it was noted that location information was discernible from tweets @xcite . this was followed by typhoon pablo in the philippines in the same year where tweets were systematically analyzed and categorized @xcite . however, the first twitter classifier was developed after the oklahoma tornadoes of 2013. this classifier, which was deployed during the crisis, and used to classify the severity of need for directing aid appropriately @xcite . @xcite is the first survey that we are aware of in the crisis informatics space as it relates to social media. the survey was not entirely focused on language technologies per se, but, rather, reviewed the academic literature that described the extraction of crisis-relevant content from social media, including monitoring, event detection, social media content harvesting, etc. their survey focused on nlp as a pre-processing step, i.e., to filter out irrelevant content, with a very limited review of nlp used in tweet classification. @xcite reviewed the literature on applying ai in the disaster management life-cycle, thoroughly describing the life-cycle and how ai might apply, yet they gave very little background on nlp in that context. @xcite also surveyed the literature from the perspective of disaster management, giving a thorough survey of papers on social media for situational awareness, with extensive background on nlp as applied to classifying and processing social media, including content, sentiment, user, and temporal classification. @xcite restricted their paper search to those focused on tools, their potential utility in crisis management, and recommendations for future work on adapting the technology better to the target audience of crisis management decision makers. @xcite is one of two papers @xcite . ## paper selection our systematic review follows the preferred reporting items for systematic reviews and meta-analyses (prisma) guidelines @xcite . we gathered a large number of relevant english articles published in the past five years, from january 2020 to december 2024. the process is illustrated in figure 1 , as explained below. ## corpus construction out of the 355 papers in our final collection, 91 (25.6%) focus on corpus construction ("corpus papers"). in this section, we discuss the properties of the corpora with respect to modality, language, social media platform, and annotation type (see figures 3 4 5 6 7 ). each figure in this section has two pie charts: the left shows the numbers of corpora presented in the corpora papers, and the right shows the numbers of corpora used by the system papers. the full list of corpus papers and the basic information on the corresponding corpora are in tables 2-6 in appendix b. in addition, some wellknown datasets released before 2020 are in ## nlp system development of 355 papers included in this survey, 215 (60.6%) focus on system development ("system papers"). ## real-life applications and deployment nlp systems can potentially be used to assist crisis management in many ways, such as message triaging for humanitarian organizations @xcite , emergent event detection @xcite , geo-location for rescue efforts and situational assessment @xcite , generation of situation reports and crisis maps @xcite , monitoring and analyzing public emotions and responses @xcite , and helping the public acquire/process information @xcite . however, there are only 26 application papers that describe systems that attempt to address the "application" of lt to real-life situations (e.g., to help aid providers). of these, it is not clear how many have been adopted by the crisis community. this indicates a surprising gap given that one would assume that the system development work being carried out by lt researchers (described in §5) is intended to be used in actual crises. ## challenges and future directions our survey has shown that there has been a significant amount of work that has been done over just the past five years applying lt to crisis management. that said, there are still many challenges to be addressed. we highlight 6 primary challenges and possible future directions in this section. ## conclusion in reviewing the hundreds of papers for this survey, it was obvious throughout almost all of them that the work was being done with good intent: most papers spoke directly to the need to provide aid in crisis situations, and many authors highlighted how their work could help. it was clear that the authors were doing their work with an eye on the greater good. this is laudable and utterly inspiring. in fact, it makes us proud to be lt researchers. that said, good intentions cannot operate in a vacuum. an important question must be asked: is the work being done for any particular task being done based on perceived need, or being done based on actual need? if the former, then that disconnect might mean that the work we are doing, no matter how inspiring, may not be consumed by those we think might need it most. it does not diminish the work being done, but it does mean that our lofty aspirations might not be met. the solution is simple: we should engage with the broader crisis community, e.g., aid providers, ngos, government bodies, affected communities (including language communities), crisis informatics researchers, crisis or disaster managers (including those operating in a local theater), and any others who engage in crisis response work. this is not necessarily something each individual member of our research community would need to or should take on, but rather the lt community writ large, specifically those who wish to take on the daunting tasks of creating lt4cpr. the mere fact that there a few hundred papers written over the past five years in the lt4cpr space (per appendix b and figure 2 ) speaks volumes. lt4cpr is not just a passing fad nor some fancy new algorithm: those of us involved are genuinely interested, as a field, in improving the lives of others; indeed, as witnessed so many years ago in haiti, in saving the lives of others. we hope our survey will generate even more interest across the language technology disciplines in lt4cpr and that it will offer suggestions of differing research paths for those already involved. there is much that has already been done. but there is also so much more that we can do.