## 2 Literature Review
Processing millions of tweets to identify eyewitness reports remains a perplexing task and an open research area. [PERSON] et al. (2020) presented and evaluated a theoretical model for identifying witness accounts and related accounts for various events. The study was evaluated using the incidents of shark sightings, concerts, protests, and cyclones, and the findings describe the impact of various factors that directly affect witnessing characteristics. Similarly, [PERSON] and [PERSON] (2020) presented a set of linguistic features to categorize disaster event-related tweets into two categories of \"eyewitness\" and \"non-eyewitness\". The study utilized linguistic features such as filters to calculate the semantic similarity of the documents. The identified features demonstrated their importance in surfing the breaking news from social media platforms. Various researchers further exploit the importance of linguistic features. For instance, [PERSON] et al. (2020) proposed an eyewitness identification approach by combining linguistics with meta-features. For topic classification, the authors adapted the word dictionary of LIWC ([[http://www.liwc.net/](http://www.liwc.net/)]([http://www.liwc.net/](http://www.liwc.net/)) accessed on 5 August 2021) and OpenCalais ([[http://www.opencalais.com/opencalais-api/](http://www.opencalais.com/opencalais-api/)]([http://www.opencalais.com/opencalais-api/](http://www.opencalais.com/opencalais-api/)) accessed on 5 August 2021) API. Similarly, [PERSON] et al. (2020) presented a set of eyewitness features using metadata, stylistic, lexical, and semantics characteristics. The semantics dimensions cater to the \"category definitions words\", e.g., \"Shelter Needed\". The summary of eyewitness feature-based techniques discussed above is presented in Table 2 with findings and limitations. The critical analysis shows that the research community has previously exploited different features to identify direct eyewitness sources, which are considered more credible. The existing research either focuses on retrieving location-related keywords or users' location information from Twitter metadata to search for the local residents of disaster-hit areas. However, identifying location-related keywords does not guarantee that a disaster event has occurred at the location mentioned in the tweets. Using users' location through GPS information attached to their posts is more reliable; however, the research reveals that only 1-3% of Tweets are geotagged, and relying solely on them may not provide enough data for decision-making (2020; 2021). Recently, [PERSON] et al. (2020) developed a comprehensive taxonomy and list of features to identify direct eyewitness posts independent from platform-specific metadata such as geoid. The authors proposed a methodology to categorize tweets using expert-driven engineering for disaster events. They identified a comprehensive feature list called domain-expert features based on their previous work, [PERSON] et al. (2020). The technique combines the textual features (bag of words) with domain-expert features to identify whether a source is an eyewitness or not. However, the work utilized a static-dictionary-based approach to annotate the Twitter dataset for classification. In this research, we have proposed the rule-based linguistic approach to automatically identify eyewitness tweets without relying on platform-specific metadata or human intervention. Hence, the approach is scalable to any domain and can be adopted to any type of disaster event.