###### Abstract  
Emergency response systems require precise and accurate information about an incident to respond accordingly. An eyewitness report is one of the sources of such information. The research community has proposed diverse techniques to identify eyewitness messages from social media platforms. In our previous work, we created grammar rules by exploiting the language structure, linguistics, and word relations to automatically extract feature words to classify eyewitness messages for different disaster types. Our previous work adopted a manual classification technique and secured the maximum F-Score of 0.81, far less than the static dictionary-based approach with an F-Score of 0.92. In this work, we enhanced our work by adding more features and fine-tuning the Linguistic Rules to identify feature words related to Twitter Eyewitness messages for Disaster events, named as LR-TED approach. We used linguistic characteristics and labeled datasets to train several machine learning and deep learning classifiers for classifying eyewitness messages and secured a maximum F-score of 0.93. The proposed LR-TED can process millions of tweets in real-time and is scalable to diverse events and unseen content. In contrast, the static dictionary-based approaches require domain experts to create dictionaries of related words for all the identified features and disaster types. Additionally, LR-TED can be evaluated on different social media platforms to identify eyewitness reports for various disaster types in the future. linguistic rules; machine learning; deep learning; social media; eyewitness identification; disaster response +
Footnote †: journal: Accepted: 29 September 2022  
and emergency alerts [11], response systems [12; 13], etc. Analyzing such data is essential for event detection, especially in emergency conditions due to natural disasters such as floods, hurricanes, fires, or earthquakes [1; 14]. The studies have proven the importance of the credible information required for responding to such emergency conditions and that the eyewitness of the event can be a valuable resource for information relating to the events [14]. Twitter has proven its potential to be considered a news breaker. Studies have proven that 85% of Twitter's trending topics are news headlines [3]. How does Twitter do that? The answer is by extracting valuable information from the tweets of eyewitness users. Following are some events that prove the capacity of Twitter for breaking the news:  
1. Delta Aircraft flight's Emergency landing ([[http://channelnewsasia.com/news/world/delta-flight-middle-of-the-ocean-seattle-beijing-emergency-land-11062706](http://channelnewsasia.com/news/world/delta-flight-middle-of-the-ocean-seattle-beijing-emergency-land-11062706)]([http://channelnewsasia.com/news/world/delta-flight-middle-of-the-ocean-seattle-beijing-emergency-land-11062706](http://channelnewsasia.com/news/world/delta-flight-middle-of-the-ocean-seattle-beijing-emergency-land-11062706)), accessed on 20 July 2021): A tweet posted by one of the passengers of the flight about its emergency landing on a remote island in Alaska due to the potential engine failure was the source for the news agency about the incident. 2. California Earthquake ([[https://latimesblogs.latimes.com/technology/2008/07/twitter-earthqu.html](https://latimesblogs.latimes.com/technology/2008/07/twitter-earthqu.html)]([https://latimesblogs.latimes.com/technology/2008/07/twitter-earthqu.html](https://latimesblogs.latimes.com/technology/2008/07/twitter-earthqu.html)), accessed on 20 July 2021): Half a dozen tweets were available on Twitter about the earthquake, a minute before the recorded USGS ([[https://www.usgs.gov/](https://www.usgs.gov/)]([https://www.usgs.gov/](https://www.usgs.gov/)), accessed on 20 July 2021) time. 3.