You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Intelligent Disaster Response via Social Media Analysis A Survey 
Chapter Title:  Event Detection Methods 
1. Abstract of Cited Paper (BIB001):  Social media provides researchers with continuously updated information about developments of interest to large audiences. This paper addresses the task of identifying controversial events using Twitter as a starting point: we propose 3 models for this task and report encouraging initial results. 
2. Abstract of Cited Paper (BIB002):  Twitter, a popular microblogging service, has received much attention recently. An important characteristic of Twitter is its real-time nature. For example, when an earthquake occurs, people make many Twitter posts (tweets) related to the earthquake, which enables detection of earthquake occurrence promptly, simply by observing the tweets. As described in this paper, we investigate the real-time interaction of events such as earthquakes in Twitter and propose an algorithm to monitor tweets and to detect a target event. To detect a target event, we devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, we produce a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location. We consider each Twitter user as a sensor and apply Kalman filtering and particle filtering, which are widely used for location estimation in ubiquitous/pervasive computing. The particle filter works better than other comparable methods for estimating the centers of earthquakes and the trajectories of typhoons. As an application, we construct an earthquake reporting system in Japan. Because of the numerous earthquakes and the large number of Twitter users throughout the country, we can detect an earthquake with high probability (96% of earthquakes of Japan Meteorological Agency (JMA) seismic intensity scale 3 or more are detected) merely by monitoring tweets. Our system detects earthquakes promptly and sends e-mails to registered users. Notification is delivered much faster than the announcements that are broadcast by the JMA. 
3. Abstract of Cited Paper (BIB003):  We demonstrate a system for automatically augmenting information on planned events with Twitter messages, aimed at enhancing the event information-seeking experience with rich and timely user-contributed content. Our system uses a set of automatic query building strategies that, for any given event and its associated context features (e.g., title, description, location), identify and return related Twitter messages. We present two alternative interfaces to our system, namely, a browser plug-in and a customizable Web interface. The browser plug-in uses a precongured set of strategies to display related Twitter messages alongside the event information on a user-contributed event Website. The customizable interface provides the exibility of tuning the query building strategies, and also allows for several alternative ranking modes for the identied event messages. 
4. Abstract of Cited Paper (BIB004):  User-contributed messages on social media sites such as Twitter have emerged aspowerful, real-time means of information sharing on the Web. These short messages tend to reflect a variety of events in real time, making Twitter particularly well suited as a source of real-time event content. In this paper, we explore approaches for analyzing the stream of Twitter messages to distinguish between messages about real-world events andnon-event messages. Our approach relies on a rich family of aggregatestatistics of topically similar message clusters. Large-scale experiments over millions of Twitter messages show the effectiveness of our approach for surfacing real-world event content on Twitter. 
5. Abstract of Cited Paper (BIB005):  This paper describes methods for automatically detecting events involving known entities from Twitter and understanding both the events as well as the audience reaction to them. We show that NLP techniques can be used to extract events, their main actors and the audience reactions with encouraging results. 
6. Abstract of Cited Paper (BIB006):  The Emergency Situation Awareness (ESA) system provides all-hazard situation awareness information for emergency managers using content gathered from the public Twitter API. It collects, filters and analyses Tweets from specific regions of interest in near-real-time, enabling effective alerting for unexpected incidents and monitoring of emergency events with results accessible via an interactive website. 
7. Abstract of Cited Paper (BIB007):  Twitter is among the fastest-growing microblogging and online social networking services. Messages posted on Twitter tweets have been reporting everything from daily life stories to the latest local and global news and events. Monitoring and analyzing this rich and continuous user-generated content can yield unprecedentedly valuable information, enabling users and organizations to acquire actionable knowledge. This article provides a survey of techniques for event detection from Twitter streams. These techniques aim at finding real-world occurrences that unfold over space and time. In contrast to conventional media, event detection from Twitter streams poses new challenges. Twitter streams contain large amounts of meaningless messages and polluted content, which negatively affect the detection performance. In addition, traditional text mining techniques are not suitable, because of the short length of tweets, the large number of spelling and grammatical errors, and the frequent use of informal and mixed language. Event detection techniques presented in literature address these issues by adapting techniques from various fields to the uniqueness of Twitter. This article classifies these techniques according to the event type, detection task, and detection method and discusses commonly used features. Finally, it highlights the need for public benchmarks to evaluate the performance of different detection approaches and various features. 
8. Abstract of Cited Paper (BIB008):  With the increase in GPS-enabled devices, socialmedia sites, such as Twitter, are quickly becoming a primeoutlet for timely geo-spatial data. Such data can be leveragedto aid in emergency response planning and recovery operations. Unfortunately, the information overload poses significantdifficulty to the quick discovery and identification ofemergency situation areas. The system tackles this challengeby providing real-time mapping of influence areas based onautomatic analysis of the flow of discussion using languagedistributions. The workflow is then further enhanced throughthe addition of keyword surprise mapping which projects the general divergence map onto specific task-level keywords for precise and focused response.

Literature Review:
Events are real-world occurrences that unfold over space and time and. The goal of event detection methods is extracting events in a stream of news or social media posts BIB001 . Event detection using social media has been extensively studied and the different categorizations are available for proposed methods in this area. When there is no information about future events available, the event detection method falls into the unspecified category. In this category, detection methods are based on bursts or trends in the stream of posts BIB002 . In the specified event detection methods, contextual information such as time and venue are available for the anticipated event BIB004 . Another categorization of events is new versus retrospective. New event detection is extracting previously unseen events from a stream of posts as they come. Retrospective event detection also finds unseen events but the data source is an accumulation of historic posts BIB001 . Clustering methods are the most common in detecting both types of events BIB008 . But there are also supervised methods such as Naive Bayes BIB005 and gradient boosted decision trees that have been used for new event detection BIB006 . Clustering methods focus on documents and grouping them based on similarities, i.e. they are document-pivot. There is a group of feature-pivot techniques that use changes and bursts in features of documents to detect events. These features include frequency of specific keywords BIB007 , surprise level of relevant keywords BIB009 , and statistical features of posts (i.e. word frequencies) BIB003 .