## 3 Methodology
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**State-of-the-Art** & **Concept** & **Approach** & **Findings** & **Shortcomings** \\ \hline [PERSON] et al. [19] & This work proposed the idea of finding trustworthy information sources. & The authors adopted the human-centered design approach to developing the SRSR system that works on tweets by journalists. & Achoived accuracy between 0-54\%. & Achoted manual dictionaries and no features to identify the eyewitness. \\ \hline [PERSON] et al. [20] & The author studied the role of social media during disaster events. & The author studied the recommendation system using temporal information and evaluated the approach for different crises. & Achoived accuracy between 0-54\%. & It requires the user and network information to proceed. No features to identify the eyewitness. \\ \hline [PERSON] et al. [22] & The author presented the idea of identifying non-geotagged tweets from the crises region. & The author presented the idea of patterns and proposed an automatic way to classify them as non-geotagged tweets from crisis regions. & Achieved F-Score of patterns and proposed an automatic way to classify them Boston Bombing incidents. & Achieved F-Score of patterns and proposed an automatic way to classify them features to identify the eyewitness. \\ \hline [PERSON] et al. [23] & This work explored the idea of identifying accounts and their related accounts. & The author presented the linguistic features and applied them as filters for the identification of eyewitness reports. & Achieved an average F1 score of 89.7\%. & Used static dictionary of terms. Requires language and location information. \\ \hline [PERSON] and [PERSON] [24] & Presented a filtration-based technique to identify eyewitnesses. & The author presented the linguistic features and applied them as filters for the identification of eyewitness reports. & Achoived a degree of 62\% is achieved. & Require geo-location to identify eyewitness events. \\ \hline [PERSON] et al. [25] & Presented a hybrid approach to identify eyewitnesses during emergency events. & The author presented the linguistic features and applied them as filters for the identification of eyewitnesses. & Achieved a degree of 89.7\%. & Achoived an average F1 score of 89.7\%. & Used static dictionary of terms. Requires language and location information. \\ \hline [PERSON] et al. [26] & Presented a set of syntax and linguistic-based features for event detection. & Applied an unsupervised approach to news articles to detect the events. & Achieved 42\% precision and 66\% recall. & Domain-specific data of news to detect the events. \\ \hline [PERSON] et al. [14] & The authors manually investigated the tweet sources related to eyewitnesses and classified them into thirteen features for identification. & Experts of related domains manually analyzed the tweets to identify the proposed features from the tweet content. Feature words were extracted for all thirteen features. & F-Score of 0.917. & Manual implementation. Failed to Implement all characteristics. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of Eyewitness Identification Techniques.