This document summarizes a student's dissertation on classifying and detecting disaster tweets based on machine learning. The student collected tweets related to disasters like floods and earthquakes to build a dataset. Various machine learning classification algorithms were tested on the dataset, with logistic regression achieving the highest accuracy of 78%. By combining results from all algorithms, an overall accuracy of 79% was achieved in identifying actual disaster tweets. The research aims to help disaster management by providing real-time social media analysis during emergencies.

1. CHONGQING UNIVERSITY OF TECHNOLOGY
Name: Akram Ali (艾历)
Student ID: 62017010094
Major: Computer Science and
Technology
DISASTER TWEETS CLASSIFICATION AND
DETECTION BASED ON MACHINE LEARNING

2. Introduction
When disaster strikes, people post real-time updates on social media sites (such as Twitter);
this information is extremely useful for disaster relief and response teams, as it allows them
to prioritize activities instantly. Text mining and machine learning algorithms can sift through
massive amounts of unstructured data on social media sites like Twitter for disaster-related
information using keywords and phrases. One difficulty the computer may have is
determining if a tweet text is referring to a real tragedy or is using those keywords as a
metaphor, which could result in massive mislabeling of tweets. As a result, this study seeks to
identify between actual and fraudulent disaster tweets using Natural Language Processing
(NLP) and classification models.

3. Disaster & Social Media
A disaster is defined as "a potentially traumatic event that affects a large number of people
at once, has a rapid beginning, and is time-limited." Natural disasters (such as earthquakes
or hurricanes), technology disasters (such as an oil leak), and human disasters (such as
terrorism) can all result in 'physical, social, psychological, sociodemographic,
socioeconomic, and political effects.' Normally, disasters are thought of in stages, with a
pre-event, event, and post-event phase.
Social media, often known as social networking or Web, refers to a group of web-based
platforms and services that allow users to create public or semi-public profiles or material,
as well as communicate with other users' profiles or content. Typically, social media may be
accessed by a variety of computing devices, including desktop or laptop computers,
cellphones, and tablets. Social networking has become prevalent in various industries,
including disaster. Catastrophe planners, responders, and investigators are frequently
hopeful about social media's ability to improve disaster communication and operations.

4. Machine Learning
Machine learning is the branch of science that enables machines to learn without having
to be programmed explicitly. One of the most exciting advancements ever seen is ML.
This gives the machine the ability to learn, which, as the name suggests, makes it more
human-like. Machine learning is being applied in a lot more places these days than one
may think. Computational statistics, which focuses on computer-based predictions, is
closely related to machine learning. Mathematical optimization research contributes
methodologies, theory, and application fields to the subject of machine learning. Data
mining is a branch of machine learning that focuses on exploratory data exploration
through unsupervised learning.

5. Classification of Machine Learning

6. Dataset
To train and validate our model, sufficient tweets are required in relation to an event
that should represent the realistic scenario of that event. In our work, we have collected
tweets about different types of disasters. We have used Twitter API to capture live
tweets related to Disasters like floods, hailstorms, bushfires, earthquakes, etc. Here is the
dataset view:

7. Result
With the analysis of different types of algorithms, we got the Accuracy Result from
them. AS measuring their performance of Recall, Precision, F1 Score we had gotten The
Logistic Regression Classifier Algorithm had a better Accuracy resultant of 78%. But, we
had combined all algorithms result and get total 79% Accuracy. Comparison of all the
Algorithms given below:

8. Conclusion
we proposed a system for real-time analysis of social media information while successfully
collecting and maintaining social media content. This research will also aid in raising public
awareness of the dangers of a disaster. It will function as a warning element for disaster
management agencies. The textual content of tweets was only used for classification in this
study, and any Internet links given by (if any) tweets were discarded. The disadvantage is
that such internet links can lead to websites with further information or photographs of the
location in question. Other researchers can go various additional avenues as a result of the
current study. The classification accuracy is 79 percent, and it can be improved even more
by considering more characteristics. More people expressing their opinions in their mother
tongue will strengthen the system as more languages are added.

9. References
1) Kabra, G., & Ramesh, A. (2015). “Analyzing ict issues in humanitarian supply chain
management: A sap-lap linkages framework. Global Journal of Flexible Systems
Management”, 16(2), 157–171.
2) Lindsay, B. R. (2011). “Social media and disasters: Current uses, future options and policy
considerations. Congressional research service reports”, 13.
3) Adnan, Kiran-Akbar, Rehan-2019 “An analytical study of information extraction from
unstructured and multidimensional big data”
4) d. boyd, S. Golder, and G. Lotan, “Tweet, tweet, retweet: Conversational aspects of retweeting
on Twitter”, in Proceedings of the 43rd Hawaii International Conference on System Sciences,
2010, pp. 1-10.
5) A.L. Hughes and L. Palen, "Twitter adoption and use in mass convergence and emergency
events", International Journal of Emergency Management, vol. 6, 2009, pp. 248- 260
6) Graham Neubig and Yuichiroh Matsubayashi-2011, in theier work “Safety Information Mining
What can NLP do in a disaster Graham”
7) John Lingad, Sarvnaz Karimi, Jie Yin -2013, “Location Extraction From Disaster-Related
Microblogs”

10. Thank You