Social Media

Assessing post-disaster recovery using sentiment analysis: The case of L’Aquila, Italy, 2021

Memorial days of disasters represent an opportunity to evaluate the progress of recovery. This article uses sentiment analysis (SA) to assess post-disaster recovery on the 10th anniversary of L'Aquila's earthquake using Twitter data. We have analyzed 4349 tweets from 4 to 10 April 2019 with the hashtag: #L'Aquila that we have obtained from a third-party vendor. The polarity is first defined using a supervised classification based on experts' rules on post-disaster reconstruction and Grammarly tones. Then, this polarity is compared with the outcome of an unsupervised classification based on the pre-trained SA machine learning algorithm developed by MonkeyLearn. We have found a significant negative assessment of the post-disaster recovery process in L'Aquila. About 33.1% of the tweets had a negative polarity, followed by 29.3% tweets with a neutral polarity, 28.7% with positive polarity, and 8.9% unrelated to the anniversary. Further analysis of the tweets confirms that after 10 years, the reconstruction is still ongoing and that criticism of the recovery reported in the literature is also found in the tweets. Based on our analysis, the critical day to collect most of the data is the anniversary's exact day. Tweets from citizens and/or news agencies, which are more likely to express the reality experienced, are therefore more useful in understanding recovery than tweets from government officials and/or governmental institutions. From the total 4349 tweets, we can state that 2488 (57%) were correctly classified by the pre-trained SA machine learning algorithm developed by MonkeyLearn, while 1861 (43%) were misclassified. It means an overall accuracy (ACC) of 57% and a misclassification rate of 43% by the algorithm. We argue that our results have the potential to serve as a benchmark that can be used to compare other post-disaster recovery processes using the same Twitterbased SA on their anniversaries.

2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2015

Sociology Study

Natural Hazards

Traditionally, earthquake impact assessments have been made via fieldwork by non-governmental organisations (NGO's) sponsored data collection; however, this approach is time-consuming, expensive and often limited. Recently, social media (SM) has become a valuable tool for quickly collecting large amounts of first-hand data after a disaster and shows great potential for decision-making. Nevertheless, extracting meaningful information from SM is an ongoing area of research. This paper tests the accuracy of the pre-trained sentiment analysis (SA) model developed by the no-code machine learning platform MonkeyLearn using the text data related to the emergency response and early recovery phase of the three major earthquakes that struck Albania on the 26th November 2019. These events caused 51 deaths, 3000 injuries and extensive damage. We obtained 695 tweets with the hashtags: #Albania #AlbanianEarthquake, and #albanianearthquake from the 26th November 2019 to the 3rd February 2020. ...

IRJET, 2022

DetectingTheDamageAssessmentTweetsIsBeneficialToB othHumanitarianOrganizations And Victims During A Disaster. By far most Of The Previous Works That Identify Tweets During A Disaster Have Been Related To Situational Information, Availability/Requirement Of Resources, Infrastructure Damage, Etc. There Are Only A Few Works Focused On Detecting The Damage Assessment Tweets. A Novel Method Is Proposed For Identifying The Damage Assessment Tweets During A Disaster. The Proposed Method Effectively Utilizes The Low-Level Lexical Features, Top-Most Frequency Word Features, And Syntactic Features That Are Specific To Damage Assessment. These Features are Weighted By Using Simple LSTM (Long Short Term Memory) And Tensor Flow Frame work Algorithms. Further, Random Forest Technique Is Used As A Classifier For Classifying The Tweets. Examined 14 Standard Disaster Datasets Of Different Categories For Binary And Multi-Class Classification. Most Importantly, The Proposed Method Can Be Applied In A Situation Where Enough Labeled Tweets Are Not Available And Also When Specific Disaster Type Tweets Are Not Available. This Can Be Done By Training The Model With Past Disaster Datasets

Advances in Intelligent Systems and Computing, 2019

Emergency situations generate a high requirement for information, and on the other hand diminish its availability. In the last decade, intellectuals and government authorities have assessed the potential of information circulating through social networks, mainly the one originated from natural disasters. Because of its direct and fast way of communication, and because of the reach of its network, Twitter® is the most used social platform for crisis management. Twitter analytics is a rising area of study. The goal of this research is to analyze the time and content scopes of a significant dataset of tweets in the first 72 h of the 2017 Mexico earthquake around three official profiles. The methodology used is based on text mining techniques; the tweets have been classified into five categories based on the purpose, responses and behavior of both the authorities and the public. The results indicate that the messages about actions, information, and opinion categories predominated over emotions, and technology.

2020

Social media is the main source of providing information in the form of user generated content(UGC) on the severity of Natural disaster. However, extracting the relevant information in an organized manner from social media has been a challenging task. Therefore, the purpose of this research paper is to provide an efficient algorithm that can reduce the workforce of disaster management by classifying relevant social media streams, humanitarian aid, and damage assessment. The project will classify relevant tweets of hurricane harvey, hurricane irma, california wildfire, mexico earthquake, Nepal earthquake, and iran-iraq earthquake by applying Natural language processing and computer vision based deep learning models ensemble together. The first task will include the categorization of tweets and their respective images on the basis of their relevance(containing information about natural disasters). Secondly, the image data will be categorized further based on humanitarian aid which includes injured or dead people, infrastructure damage, vehicle damage, and missing or found people. Finally, the damage assessment of the events will be categorized based on mild, moderate, and severe. The demo application will be developed to provide a user interface for natural disaster management teams to access disaster.

Intensity-Based Sentiment and Topic Analysis. The Case of the 2020 Aegean Earthquake, 2022

After an earthquake, it is necessary to understand its impact to provide relief and plan recovery. Social media (SM) and crowdsourcing platforms have recently become valuable tools for quickly collecting large amounts of first-hand data after a disaster. Earthquakerelated studies propose using data mining and natural language processing (NLP) for damage detection and emergency response assessment. Using tex-data provided by the Euro-Mediterranean Seismological Centre (EMSC) collected through the LastQuake app for the Aegean Earthquake, we undertake a sentiment and topic analysis according to the intensities reported by their users in the Modified Mercalli Intensity (MMI) scale. There were collected 2,518 comments, reporting intensities from I to X being the most frequent intensity reported III. We use supervised classification according to a rule-set defined by authors and a two-tailed Pearson correlation to find statistical relationships between intensities reported in the MMI by LastQuake app users, polarities, and topics addressed in their comments. The most frequent word among comments was: "Felt." The sentiment analysis (SA) indicates that the positive polarity prevails in the comments associated with the lowest intensities reported: (I-II), while the negative polarity in the comments is associated with higher intensities (III-VIII and X). The correlation analysis identifies a negative correlation between the increase in the reported MMI intensity and the comments with positive polarity. The most addressed topic in the comments from LastQuake app users was intensity, followed by seismic information, solidarity messages, emergency response, unrelated topics, building damages, tsunami effects, preparedness, and geotechnical effects. Intensities reported in the MMI are significantly and negatively correlated with the number of topics addressed in comments. Positive polarity decreases with the soar in the reported intensity in MMI demonstrated the validity of our first hypothesis, despite not finding a correlation with negative polarity. Instead, we could not prove that building damage, geotechnical effects, lifelines affected, and tsunami effects were topis addressed only in comments reporting the highest intensities in the MMI.

2013