Intelligent Disaster Response via Social Media Analysis A Survey <s> Location Estimation <s> We propose and evaluate a probabilistic framework for estimating a Twitter user's city-level location based purely on the content of the user's tweets, even in the absence of any other geospatial cues. By augmenting the massive human-powered sensing capabilities of Twitter and related microblogging services with content-derived location information, this framework can overcome the sparsity of geo-enabled features in these services and enable new location-based personalized information services, the targeting of regional advertisements, and so on. Three of the key features of the proposed approach are: (i) its reliance purely on tweet content, meaning no need for user IP information, private login information, or external knowledge bases; (ii) a classification component for automatically identifying words in tweets with a strong local geo-scope; and (iii) a lattice-based neighborhood smoothing model for refining a user's location estimate. The system estimates k possible locations for each user in descending order of confidence. On average we find that the location estimates converge quickly (needing just 100s of tweets), placing 51% of Twitter users within 100 miles of their actual location. <s> BIB001 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Location Estimation <s> Geography and social relationships are inextricably intertwined; the people we interact with on a daily basis almost always live near us. As people spend more time online, data regarding these two dimensions -- geography and social relationships -- are becoming increasingly precise, allowing us to build reliable models to describe their interaction. These models have important implications in the design of location-based services, security intrusion detection, and social media supporting local communities. Using user-supplied address data and the network of associations between members of the Facebook social network, we can directly observe and measure the relationship between geography and friendship. Using these measurements, we introduce an algorithm that predicts the location of an individual from a sparse set of located users with performance that exceeds IP-based geolocation. This algorithm is efficient and scalable, and could be run on a network containing hundreds of millions of users. <s> BIB002 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Location Estimation <s> We implement a city-level geolocation prediction system for Twitter users. The system infers a user’s location based on both tweet text and user-declared metadata using a stacking approach. We demonstrate that the stacking method substantially outperforms benchmark methods, achieving 49% accuracy on a benchmark dataset. We further evaluate our method on a recent crawl of Twitter data to investigate the impact of temporal factors on model generalisation. Our results suggest that user-declared location metadata is more sensitive to temporal change than the text of Twitter messages. We also describe two ways of accessing/demoing our system. <s> BIB003 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Location Estimation <s> Real-time information from microblogs like Twitter is useful for different applications such as market research, opinion mining, and crisis management. For many of those messages, location information is required to derive useful insights. Today, however, only around 1% of all tweets are explicitly geotagged. We propose the first multi-indicator method for determining (1) the location where a tweet was created as well as (2) the location of the user's residence. Our method is based on various weighted indicators, including the names of places that appear in the text message, dedicated location entries, and additional information from the user profile. An evaluation shows that our method is capable of locating 92% of all tweets with a median accuracy of below 30km, as well as predicting the user's residence with a median accuracy of below 5.1km. With that level of accuracy, our approach significantly outperforms existing work. <s> BIB004 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Location Estimation <s> Social networks are often grounded in spatial locality where individuals form relationships with those they meet nearby. However, the location of individuals in online social networking platforms is often unknown. Prior approaches have tried to infer individuals' locations from the content they produce online or their online relations, but often are limited by the available location-related data. We propose a new method for social networks that accurately infers locations for nearly all of individuals by spatially propagating location assignments through the social network, using only a small number of initial locations. In five experiments, we demonstrate the effectiveness in multiple social networking platforms, using both precise and noisy data to start the inference, and present heuristics for improving performance. In one experiment, we demonstrate the ability to infer the locations of a group of users who generate over 74% of the daily Twitter message volume with an estimated median location error of 10km. Our results open the possibility of gathering large quantities of location-annotated data from social media platforms. <s> BIB005 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Location Estimation <s> Disaster response agencies have started to incorporate social media as a source of fast-breaking information to understand the needs of people affected by the many crises that occur around the world. These agencies look for tweets from within the region affected by the crisis to get the latest updates of the status of the affected region. However only 1% of all tweets are geotagged with explicit location information. First responders lose valuable information because they cannot assess the origin of many of the tweets they collect. In this work we seek to identify non-geotagged tweets that originate from within the crisis region. Towards this, we address three questions: (1) is there a difference between the language of tweets originating within a crisis region and tweets originating outside the region, (2) what are the linguistic patterns that can be used to differentiate within-region and outside-region tweets, and (3) for non-geotagged tweets, can we automatically identify those originating within the crisis region in real-time? <s> BIB006 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Location Estimation <s> The growing popularity of Twitter as an information medium has allowed unprecedented access to first-hand information during crises and mass emergency situations. Due to the sheer volume of information generated during a disaster, a key challenge is to filter tweets from the crisis region so their analysis can be prioritized. In this paper, we introduce the task of identifying whether a tweet is generated from crisis regions and formulate it as a decision problem. This problem is challenging due to the fact that only ~1% of all tweets have location information. Existing approaches tackle this problem by predicting the location of the user using historical tweets from users or their social network. As collecting historical information is not practical during emergency situations, we investigate whether it is possible to determine that a tweet originates from the crisis region through the information in the tweet and the publishing user's profile. <s> BIB007 </s> Intelligent Disaster Response via Social Media Analysis A Survey <s> Location Estimation <s> Crisis informatics is a multidisciplinary field combining computing and social science knowledge of disasters; its central tenet is that people use personal information and communication technology to respond to disaster in creative ways to cope with uncertainty. We study and develop computational support for collection and sociobehavioral analysis of online participation (i.e., tweets and Facebook posts) to address challenges in disaster warning, response, and recovery. Because such data are rarely tidy, we offer lessons—learned the hard way, as we have made every mistake described below—with respect to the opportunities and limitations of social media research on crisis events. <s> BIB008
To overcome the challenge of location sparsity in social media data, several methods have been proposed to estimate the location of posts or users. Content of posts, activity characteristics, profiles, and networks of users are exploited to estimate the location in which a user is based or the post is originated from. The granularity of estimation differs from one method to another. Some approaches estimate the coordinates, some remain in the city-level, and some only focus on a disaster areas that can be limited to a neighborhood or expand to several cities or states. Content is frequently used to estimate the origin of posts. N-grams and "crisis-sensitive" features such as "in" prepositional phrase (such as "in Boston"), existential "there" (which usually describes an abstraction), and part-of-speech tag sequences are signals that discriminate in-region posts from out-region ones in course of a disaster BIB006 . Moreover, posts from a disaster area are less likely to include multiple hashtags, action words, and reference entities. Majority of such posts are original and contain URLs BIB007 . Posts from the same location frequently use similar words BIB001 and rarely use words that are used in other locations BIB003 . For locating users, the most intuitive features are geolocation or location field in their profile, the location of the websites that they linked to (which can be obtained using the IP or country code), time zone, and UTC24-Offset. These features can be combined using the stacking method  by considering an importance weight for each feature to find the most probable location of the user BIB004 . When location-indicating features are not available for a user, their location can be estimated using the location of users surrounding them. Backstrom et al. BIB002 observe that there is a power law relation between physical distance and the probability of existing a social link. Based on this finding, they propose a maximum likelihood prediction method that indicates the most probable location for a user given its neighbors. Based on triadic closure, if user a is connected to users b and c, b and c are more likely to be connected to each other  . In "Triadic heuristic" BIB005 , the location of users is estimated as the geometric median of their neighbors who are in triadic closure with them. Moreover, users are more likely to follow users nearby and more often mention the location in which they live [56] . Palen and Anderson BIB008 introduce the concept of "contextual streams" to combine Lexicon and location in order to overcome the issue of incompleteness. They use a set of broad terms (such as "frankenstorm" and "sandy" in the case of hurricane Sandy, 2012) to collect the first set of tweets. Then, they find the users who have geolocated tweets from their desired location. Finally, they collect their most recent 3200 tweets and extend their previous dataset. Using this information, they can compare the activities of users who are located on the site of the disaster before, during, and after its occurrence.