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:  Topic-Level Language Change 
1. Abstract of Cited Paper (BIB001):  Today streaming text mining plays an important role within real-time social media mining. Given the amount and cadence of the data generated by those platforms, classical text mining techniques are not suitable to deal with such new mining challenges. Event detection is no exception, available algorithms rely on text mining techniques applied to pre-known datasets processed with no restrictions about computational complexity and required execution time per document analysis. This work presents a lightweight event detection using wavelet signal analysis of hashtag occurrences in the twitter public stream. It also pro- poses a strategy to describe detected events using a Latent Dirichlet Allocation topic inference model based on Gibbs Sampling. Peak detec- tion using Continuous Wavelet Transformation achieved good results in the identification of abrupt increases on the mentions of specific hash- tags. The combination of this method with the extraction of topics from tweets with hashtag mentions proved to be a viable option to summarize detected twitter events in streaming environments. 
2. Abstract of Cited Paper (BIB002):  Twitter has become one of the largest platforms for users around the world to share anything happening around them with friends and beyond. A bursty topic in Twitter is one that triggers a surge of relevant tweets within a short time, which often reflects important events of mass interest. How to leverage Twitter for early detection of bursty topics has therefore become an important research problem with immense practical value. Despite the wealth of research work on topic modeling and analysis in Twitter, it remains a huge challenge to detect bursty topics in real-time. As existing methods can hardly scale to handle the task with the tweet stream in real-time, we propose in this paper Topic Sketch, a novel sketch-based topic model together with a set of techniques to achieve real-time detection. We evaluate our solution on a tweet stream with over 30 million tweets. Our experiment results show both efficiency and effectiveness of our approach. Especially it is also demonstrated that Topic Sketch can potentially handle hundreds of millions tweets per day which is close to the total number of daily tweets in Twitter and present bursty event in finer-granularity.

Literature Review:
A set of event detection methods are based on the assumption that burst in observing explicit topics is a sign of the occurrence of an event. Explicit topics are the ones assigned by the author of posts and are mostly known as hashtags in social media. Examples of hashtags are #frankenstorm and #hurricane in the case of Hurricane Sandy (2012). The burst is considered as an unexpected rise in the frequency or a transformation of hashtags BIB003 . Each hashtag can be represented in time-frequency space using continuous wavelet transformation on its frequency. Wavelet peaks show unusual bursts in observing that hashtag BIB002 . Occurrence of events can also be detected using hidden topics. In these methods, language is modeled as a probability distribution over topics. The assumption is that there is a fixed set of hidden topics in a corpus, each document is a random mixture of these topics, and each topic is a distribution over words. Using Latent Dirichlet Allocation (LDA) BIB001 , we can extract the probability of each topic in a document. Moreover, word distribution in each topic gives an insight on what the topic is about. To measure language change in course of a disaster, posts (such as tweets) are considered as sentences. If hidden topics of these posts largely deviate from topics of posts in regular situations, it will be considered the signal of an event. Event detection methods based on hidden topics detect abnormal topics in a specific region. Chae et al.  have extracted major topics in the area of interest using LDA. A time series based on the daily message count on each topic is generated Seasonal-Trend Decomposition Procedure Based on Loess (STL)  has been used to decompose the time series into three components: a trend component, a seasonal component, and a remainder. The remainder is supposed to be identically distributed Gaussian white noise. However, when the remainder has a large value, it indicates substantial variation in the time series. This variation can be considered as novelty or abnormality in the language. In this work, if the seven-day moving average of the remainder values has z-score higher than 2, events can be considered abnormal in 95% confidence.