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1. On Identifying Hashtags in Disaster Twitter Data

   https://doi.org/10.1609/aaai.v34i01.5387  

   Ray Chowdhury, Jishnu; Caragea, Cornelia; Caragea, Doina (June 2020, Proceedings of the AAAI Conference on Artificial Intelligence)

   null (Ed.)

   Tweet hashtags have the potential to improve the search for information during disaster events. However, there is a large number of disaster-related tweets that do not have any user-provided hashtags. Moreover, only a small number of tweets that contain actionable hashtags are useful for disaster response. To facilitate progress on automatic identification (or extraction) of disaster hashtags for Twitter data, we construct a unique dataset of disaster-related tweets annotated with hashtags useful for filtering actionable information. Using this dataset, we further investigate Long Short-Term Memory-based models within a Multi-Task Learning framework. The best performing model achieves an F1-score as high as $92.22%$. The dataset, code, and other resources are available on Github.1 

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2. A System Analytics Framework for Detecting Infrastructure-Related Topics in Disasters Using Social Sensing

   Fan, Chao; Mostafavi, Ali; Gupta, Aayush; Zhang, Cheng (January 2018, 25th International Workshop on Intelligent Computing in Engineering (EG-ICE))

   The objective of this paper is to propose and test a system analytics framework based on social sensing and text mining to detect topic evolution associated with the performance of infrastructure systems in disasters. Social media, like Twitter, as active channels of communication and information dissemination, provide insights into real-time information and first-hand experience from affected areas in mass emergencies. While the existing studies show the importance of social sensing in improving situational awareness and emergency response in disasters, the use of social sensing for detection and analysis of infrastructure systems and their resilience performance has been rather limited. This limitation is due to the lack of frameworks to model the events and topics (e.g., grid interruption and road closure) evolution associated with infrastructure systems (e.g., power, highway, airport, and oil) in times of disasters. The proposed framework detects infrastructure-related topics of the tweets posted in disasters and their evolutions by integrating searching relevant keywords, text lemmatization, Part-of-Speech (POS) tagging, TF-IDF vectorization, topic modeling by using Latent Dirichlet Allocation (LDA), and K-Means clustering. The application of the proposed framework was demonstrated in a study of infrastructure systems in Houston during Hurricane Harvey. In this case study, more than sixty thousand tweets were retrieved from 150-mile radius in Houston over 39 days. The analysis of topic detection and evolution from user-generated data were conducted, and the clusters of tweets pertaining to certain topics were mapped in networks over time. The results show that the proposed framework enables to summarize topics and track the movement of situations in different disaster phases. The analytics elements of the proposed framework can improve the recognition of infrastructure performance through text-based representation and provide evidence for decision-makers to take actionable measurements. 

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3. Seeing Should Probably not be Believing: The Role of Deceptive Support in COVID-19 Misinformation on Twitter

   https://doi.org/10.1145/3546914  

   Zuo, Chaoyuan; Banerjee, Ritwik; Shirazi, Hossein; Chaleshtori, Fateme Hashemi; Ray, Indrakshi (August 2022, Journal of Data and Information Quality)

   With the spread of the SARS-CoV-2, enormous amounts of information about the pandemic are disseminated through social media platforms such as Twitter. Social media posts often leverage the trust readers have in prestigious news agencies and cite news articles as a way of gaining credibility. Nevertheless, it is not always the case that the cited article supports the claim made in the social media post. We present a cross-genre ad hoc pipeline to identify whether the information in a Twitter post (i.e., a “Tweet”) is indeed supported by the cited news article. Our approach is empirically based on a corpus of over 46.86 million Tweets and is divided into two tasks: (i) development of models to detect Tweets containing claim and worth to be fact-checked and (ii) verifying whether the claims made in a Tweet are supported by the newswire article it cites. Unlike previous studies that detect unsubstantiated information by post hoc analysis of the patterns of propagation, we seek to identify reliable support (or the lack of it) before the misinformation begins to spread. We discover that nearly half of the Tweets (43.4%) are not factual and hence not worth checking – a significant filter, given the sheer volume of social media posts on a platform such as Twitter. Moreover, we find that among the Tweets that contain a seemingly factual claim while citing a news article as supporting evidence, at least 1% are not actually supported by the cited news, and are hence misleading. 

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4. Assessing the stability of Tweet corpora for hurricane events over time: A mixed methods approach

   Xin, E.; Murthy, D.; Lakuduva, N.; & Stephens, K. K. (January 2019, Social media + society)

   When natural disasters occur, various organizations and agencies turn to social media to understand who needs help and how they have been affected. The purpose of this study is twofold: first, to evaluate whether hurricane-related tweets have some consistency over time, and second, whether Twitter-derived content is thematically similar to other private social media data. Through a unique method of using Twitter data gathered from six different hurricanes, alongside private data collected from qualitative interviews conducted in the immediate aftermath of Hurricane Harvey, we hypothesize that there is some level of stability across hurricane-related tweet content over time that could be used for better real-time processing of social media data during natural disasters. We use latent Dirichlet allocation (LDA) to derive topics, and, using Hellinger distance as a metric, find that there is a detectable connection among hurricane topics. By uncovering some persistent thematic areas and topics in disaster-related tweets, we hope these findings can help first responders and government agencies discover urgent content in tweets more quickly and reduce the amount of human intervention needed. 

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5. Predicting Community Engagement on Twitter on Environmental Health Hazards

   https://doi.org/10.1109/ICOSC.2019.8665530  

   Alshchri, Adel; Isaacs, Wainella; Addawood, Aseel; Trotz, Maya; Chellappan, Sriram (March 2019, 2019 IEEE 13th International Conference on Semantic Computing (ICSC))

   In this empirical study, a framework was developed for binary and multi-class classification of Twitter data. We first introduce a manually built gold standard dataset of 4000 tweets related to the environmental health hazards in Barbados for the period 2014 - 2018. Then, the binary classification was used to categorize each tweet as relevant or irrelevant. Next, the multiclass classification was then used to further classify relevant tweets into four types of community engagement: reporting information, expressing negative engagement, expressing positive engagement, and asking for information. Results indicate that (combination of TF-IDF, psychometric, linguistic, sentiment and Twitter-specific features) using a Random Forest algorithm is the best feature for detecting and predicting binary classification with (87% F1 score). For multi-class classification, TF-IDF using Decision Tree algorithm was the best with (74% F1 score). 

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