## 2 Literature Review  
The effectiveness of a disaster management system relies on reliable information as input to the system. The research community has discussed the importance and effectiveness of information input to a disaster management system and its responses for relief and related recommendations [16; 17]. One of the practical examples is the Twitter Earthquake Detector (TED) program, funded by the American Recovery and Reinvestment Act. It gathers real-time earthquake-related messages and applies location, time, and keyword filtering to track earthquakes to generate alerts and reports, such as USGS ([[https://gcn.com/articles/2009/12/21/usgs-earthquake-twitter-tweets.aspx](https://gcn.com/articles/2009/12/21/usgs-earthquake-twitter-tweets.aspx)]([https://gcn.com/articles/2009/12/21/usgs-earthquake-twitter-tweets.aspx](https://gcn.com/articles/2009/12/21/usgs-earthquake-twitter-tweets.aspx)) accessed on 30 July 2021, [[https://www.usgs.gov/connect/social-media](https://www.usgs.gov/connect/social-media)]([https://www.usgs.gov/connect/social-media](https://www.usgs.gov/connect/social-media)) accessed on 30 July 2021). Similarly, San Diego State University has developed an emergency warning system (SDSU ([[https://phys.org/news/2014-10-viral-messaging-twitter-based-emergency.html](https://phys.org/news/2014-10-viral-messaging-twitter-based-emergency.html)]([https://phys.org/news/2014-10-viral-messaging-twitter-based-emergency.html](https://phys.org/news/2014-10-viral-messaging-twitter-based-emergency.html)) accessed on 30 July 2021)) based on social media platforms for broadcasting emergency warnings to San Diego citizens. The institutions responsible for responding and providing relief services need precise, credible, and accurate information [18]. The person who witnesses an event (eyewitness) can accurately offer details such as the event's intensity, precise location, number of fatalities, and other related information regarding an event. Extracting eyewit \begin{table}
\begin{tabular}{l l l} \hline \hline
**Sr.\#** & **Eyewitness Feature** & **Examples** \\ \hline
1 & Reporting small details of surroundings & “window shaking”, “water in basement” \\
2 & Words indicating perceptual senses & “seeing”, “hearing”, “feeling” \\
3 & Reporting impact of disaster & “raining”, “school canceled”, “flight delayed” \\
4 & Words indicating intensity of disaster & “intense”, “strong”, “dangerous”, “big” \\
5 & First person pronouns and adjectives & “\(i\)”, “we”, “me” \\
6 & Personalized location markers & “my office”, “our area” \\
7 & Exclamation and question marks & “\(i\)”, “?” \\
8 & Expletives & “wtf”, “omg”, “s**t**” \\
9 & Mention of a routine activity & “sleeping”, “w watching a movie” \\
10 & Time indicating words & “\(\text{now}\)”, “at the moment”, “just” \\
11 & Short tweet length & “one or two words” \\
12 & Caution and advice for others & “watch out”, “be careful” \\
13 & Mention of disaster locations & “area and street name”, “directions” \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics for Eyewitness Identification ([PERSON] et al. [14]). ness reports from a large amount of Twitter data is challenging and still an open research area. [PERSON] et al. (2019) proposed the SRSR (Seriously Rapid Source Review) system to identify the eyewitness reports from millions of tweets associated with journalism. [PERSON] et al. (2019) proposed a systematic approach for examining a diverse set of information from criminal justice and natural disasters with the help of professional emergency managers as annotators. On the other hand, [PERSON] et al. (2019) utilized the users' location information to identify local and remote users for a disaster event. Likewise, [PERSON] et al. (2020) used semantic patterns to identify a feature set to classify disaster events automatically. These studies adopted semantic patterns and location information to identify the sources. However, the concept of identifying an eyewitness account was not considered.