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Social media plays a vital role as a communication source during large-scale disasters. The unstructured and informal nature of such short individual posts makes it difficult to extract useful information, often due to a lack of additional context. The potential of social media threads– sequences of posts– has not been explored as a source of adding context and more information to the initiating post. In this research, we explored Twitter threads as an information source and developed an information extraction model capable of extracting relevant information from threads posted during disasters. We used a crowdsourcing platform to determine whether a thread adds more information to the initial tweet and defined disaster-related information present in these threads into six themes– event reporting, location, time, intensity, casualty and damage reports, and help calls. For these themes, we created the respective thematic lexicons from WordNet. Moreover, we developed and compared four information extraction models trained on GloVe, word2vec, bag-of-words, and thematic bag-of-words to extract and summarize the most critical information from the threads. Our results reveal that 70 percent of all threads add information to the initiating post for various disaster-related themes. Furthermore, the thematic bag-of-words information extraction model outperforms the other algorithms and models for preserving the highest number of disaster-related themes.

Social media platforms such as Twitter provide convenient ways to share and consume important information during disasters and emergencies. Information from bystanders and eyewitnesses can be useful for law enforcement agencies and humanitarian organizations to get firsthand and credible information about an ongoing situation to gain situational awareness among other uses. However, identification of eyewitness reports on Twitter is challenging for many reasons. This work investigates the sources of tweets and classifies them into three types (i) direct eyewitnesses, (ii) indirect eyewitness, and (iii) vulnerable accounts. Moreover, we investigate various characteristics associated with each kind of eyewitness account. We observe that words related to perceptual senses (feeling, seeing, hearing) tend to be present in direct eyewitness messages, whereas emotions, thoughts, and prayers are more common in indirect witnesses. We believe these characteristics can help make more e cient computational methods and systems in the future for automatic identification of eyewitness accounts.

Twitter is a well-known microblogging platform for rapid diffusion of views, ideas, and information. During disasters, it has widely been used to communicate evacuation plans, distribute calls for help, and assist in damage assessment. The reliability of such information is very important for decision-making in a crisis situation, but also difficult to assess. There is little research so far on the transferability of quality assessment methods from one geographic region to another. The main contribution of this research is to study Twitter usage characteristics of users based in different geographic locations during disasters. We examine tweeting activity during two earthquakes in Italy and Myanmar. We compare the granularity of geographic references used, user profile characteristics that are related to credibility, and the performance of Naïve Bayes models for classifying Tweets when used on data from a different region than the one used to train the model. Our results show similar geographic granularity for Myanmar and Italy earthquake events, but the Myanmar earthquake event has less information from locations nearby when compared to Italy. Additionally, there are significant and complex differences in user and usage characteristics, but a high performance for the Naïve Bayes classifier even when applied to data from a different geographic region. This research provides a basis for further research in credibility assessment of users reporting about disasters

Social media platforms such as Twitter provide convenient ways to share and consume important information during disasters and emergencies. Information from bystanders and eyewitnesses can be useful for law enforcement agencies and humanitarian organizations to get firsthand and credible information about an ongoing situation to gain situational awareness among other potential uses. However, the identification of eyewitness reports on Twitter is a challenging task. This work investigates different types of sources on tweets related to eyewitnesses and classifies them into three types (i) direct eyewitnesses, (ii) indirect eyewitnesses, and (iii) vulnerable eyewitnesses. Moreover, we investigate various characteristics associated with each kind of eyewitness type. We observe that words related to perceptual senses (feeling, seeing, hearing) tend to be present in direct eyewitness messages, whereas emotions, thoughts, and prayers are more common in indirect witnesses. We use these characteristics and labeled data to train several machine learning classifiers. Our results performed on several real-world Twitter datasets reveal that textual features (bag-of-words) when combined with domain-expert features achieve better classification performance. Our approach contributes a successful example for combining crowdsourced and machine learning analysis, and increases our understanding and capability of identifying valuable eyewitness reports during disasters.