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:  Situational Awareness 
1. Abstract of Cited Paper (BIB001):  During times of disasters online users generate a significant amount of data, some of which are extremely valuable for relief efforts. In this paper, we study the nature of social-media content generated during two different natural disasters. We also train a model based on conditional random fields to extract valuable information from such content. We evaluate our techniques over our two datasets through a set of carefully designed experiments. We also test our methods over a non-disaster dataset to show that our extraction model is useful for extracting information from socially-generated content in general. 
2. Abstract of Cited Paper (BIB002):  The 2011 Great East Japan Earthquake caused a wide range of problems, and as countermeasures, many aid activities were carried out. Many of these problems and aid activities were reported via Twitter. However, most problem reports and corresponding aid messages were not successfully exchanged between victims and local governments or humanitarian organizations, overwhelmed by the vast amount of information. As a result, victims could not receive necessary aid and humanitarian organizations wasted resources on redundant efforts. In this paper, we propose a method for discovering matches between problem reports and aid messages. Our system contributes to problem-solving in a large scale disaster situation by facilitating communication between victims and humanitarian organizations. 
3. Abstract of Cited Paper (BIB003):  Disaster affected communities are increasingly turning to social media for communication and coordination. This includes reports on needs (demands) and offers (supplies) of resources required during emergency situations. Identifying and matching such requests with potential responders can substantially accelerate emergency relief efforts. Current work of disaster management agencies is labor intensive, and there is substantial interest in automated tools. We present machine–learning methods to automatically identify and match needs and offers communicated via social media for items and services such as shelter, money, clothing, etc. For instance, a message such as “we are coordinating a clothing/food drive for families affected by Hurricane Sandy. If you would like to donate, DM us” can be matched with a message such as “I got a bunch of clothes I’d like to donate to hurricane sandy victims. Anyone know where/how I can do that?” Compared to traditional search, our results can significantly improve the matchmaking efforts of disaster response agencies.

Literature Review:
Informative posts provide "tactical, actionable information that can aid people in making decisions, advise others on how to obtain specific information from various sources, or offer immediate post-impact help to those affected by the mass emergency" BIB001 . Informative posts can be categorized into six groups BIB002 : caution or advice, information source (photos and videos), people (missing or found), casualties and damage (infrastructures, injured, or dead), donations (requests and offers for money/good/services), people (celebrities and authorities). One attempt toward increasing situational awareness based on social media posts is extracting requests and responses. Varga et al. BIB003 use content of tweets to extract problems and aid reports on Twitter after Japan earthquake (2010). They use the notion of "problem nucleus" and "aid nucleus" and exploit features such as trouble expressions (a manually created list of trouble expressions), excitation polarity (excitatory, inhibitory, and neutral), word sentiment polarities and word semantic word class (clusters of words such as food and disease), and location mentions to train a classifier that labels tweets as a problem or air report. Another attempt is toward matching requests with appropriate responses. Purohit et al. BIB004 propose a solution using a two-step model. In the first step, they use a binary classifier that uses n-grams and regular expressions to label tweets as requests and offers. In the next step, based on the cosine similarity of tf-idf term vectors, they match requests and offers. In this work they focus on donation related tweets and they consider money, medical, volunteer, clothing, food, and shelter requests/offers as subcategories. Their studies on Hurricane Sandy dataset (2012) shows that the majority of donations lay in "money" category.