Social networking platforms provide a vital source for disseminating information across the globe,particularly in case of disaster.These platforms are great mean to find out the real account of the disaster.Twitter is...Social networking platforms provide a vital source for disseminating information across the globe,particularly in case of disaster.These platforms are great mean to find out the real account of the disaster.Twitter is an example of such platform,which has been extensively utilized by scientific community due to its unidirectional model.It is considered a challenging task to identify eyewitness tweets about the incident from the millions of tweets shared by twitter users.Research community has proposed diverse sets of techniques to identify eyewitness account.A recent state-of-the-art approach has proposed a comprehensive set of features to identify eyewitness account.However,this approach suffers some limitation.Firstly,automatically extracting the feature-words remains a perplexing task against each feature identified by the approach.Secondly,all identified features were not incorporated in the implementation.This paper has utilized the language structure,linguistics,and word relation to achieve automatic extraction of feature-words by creating grammar rules.Additionally,all identified features were implemented which were left out by the state-of-the-art model.A generic approach is taken to cover different types of disaster such as earthquakes,floods,hurricanes,and wildfires.The proposed approach was then evaluated for all disaster-types,including earthquakes,floods,hurricanes,and fire.Based on the static dictionary,the Zahra et al.approach was able to produce an F-Score value of 0.92 for Eyewitness identification in the earthquake category.The proposed approach secured F-Score values of 0.81 in the same category.This score can be considered as a significant score without using a static dictionary.展开更多
Social media provide digitally interactional technologies to facilitate information sharing and exchanging individuals.Precisely,in case of disasters,a massive corpus is placed on platforms such as Twitter.Eyewitness ...Social media provide digitally interactional technologies to facilitate information sharing and exchanging individuals.Precisely,in case of disasters,a massive corpus is placed on platforms such as Twitter.Eyewitness accounts can benefit humanitarian organizations and agencies,but identifying the eyewitness Tweets related to the disaster from millions of Tweets is difficult.Different approaches have been developed to address this kind of problem.The recent state-of-the-art system was based on a manually created dictionary and this approach was further refined by introducing linguistic rules.However,these approaches suffer from limitations as they are dataset-dependent and not scalable.In this paper,we proposed a method to identify eyewitnesses from Twitter.To experiment,we utilized 13 features discovered by the pioneer of this domain and can classify the tweets to determine the eyewitness.Considering each feature,a dictionary of words was created with the Word Dictionary Maker algorithm,which is the crucial contribution of this research.This algorithm inputs some terms relevant to a specific feature for its initialization and then creates the words dictionary.Further,keyword matching for each feature in tweets is performed.If a feature exists in a tweet,it is termed as 1;otherwise,0.Similarly,for 13 features,we created a file that reflects features in each tweet.To classify the tweets based on features,Naïve Bayes,Random Forest,and Neural Network were utilized.The approach was implemented on different disasters like earthquakes,floods,hurricanes,and Forest fires.The results were compared with the state-of-the-art linguistic rule-based system with 0.81 F-measure values.At the same time,the proposed approach gained a 0.88 value of F-measure.The results were comparable as the proposed approach is not dataset-dependent.Therefore,it can be used for the identification of eyewitness accounts.展开更多
The study evaluated the usefulness of repeat-interviewing of witnesses to crimes who were intoxicated by alcohol at the time of the incident and their first interview, and then re-interviewed when not intoxicated the ...The study evaluated the usefulness of repeat-interviewing of witnesses to crimes who were intoxicated by alcohol at the time of the incident and their first interview, and then re-interviewed when not intoxicated the following day. Sixty young, social drinkers were divided into three groups. One group was given a “placebo” (alcohol-like) beverage, a second was given a “low dose” of alcohol (0.2 g/kg men;0.17 g/kg women), and a third was given a “high dose” of alcohol (0.6 g/kg men;0.52 g/kg women) over a 15 minute period. Twenty minutes later they viewed a 4-minute video of a crime, and afterwards they were given two opportunities to recall everything that they could remember from the incident;the first opportunity was immediately after the event, and the second was 24 hours later. Analyses of the quantity and accuracy of the details recalled revealed no overall increase in the total amount of information recalled between the first and second recall opportunities. However, on average, 18% of the details recalled by participants in the second test were new and accurate. The incidence of contradictions between the first and second recall opportunities was less than 1%. Surprisingly, none of the effects were influenced by alcohol, even at the highest dose. The results imply that 1) memory for at least some incidents observed under the influence of alcohol is resilient even up to relatively high blood-alcohol levels;and 2) the repeated interviewing of witnesses who were intoxicated at the time of the crime can reveal additional, reliable information that is not present at the initial interview, just as is the case for non-intoxicated witnesses.展开更多
文摘Social networking platforms provide a vital source for disseminating information across the globe,particularly in case of disaster.These platforms are great mean to find out the real account of the disaster.Twitter is an example of such platform,which has been extensively utilized by scientific community due to its unidirectional model.It is considered a challenging task to identify eyewitness tweets about the incident from the millions of tweets shared by twitter users.Research community has proposed diverse sets of techniques to identify eyewitness account.A recent state-of-the-art approach has proposed a comprehensive set of features to identify eyewitness account.However,this approach suffers some limitation.Firstly,automatically extracting the feature-words remains a perplexing task against each feature identified by the approach.Secondly,all identified features were not incorporated in the implementation.This paper has utilized the language structure,linguistics,and word relation to achieve automatic extraction of feature-words by creating grammar rules.Additionally,all identified features were implemented which were left out by the state-of-the-art model.A generic approach is taken to cover different types of disaster such as earthquakes,floods,hurricanes,and wildfires.The proposed approach was then evaluated for all disaster-types,including earthquakes,floods,hurricanes,and fire.Based on the static dictionary,the Zahra et al.approach was able to produce an F-Score value of 0.92 for Eyewitness identification in the earthquake category.The proposed approach secured F-Score values of 0.81 in the same category.This score can be considered as a significant score without using a static dictionary.
基金This research is funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R54)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Social media provide digitally interactional technologies to facilitate information sharing and exchanging individuals.Precisely,in case of disasters,a massive corpus is placed on platforms such as Twitter.Eyewitness accounts can benefit humanitarian organizations and agencies,but identifying the eyewitness Tweets related to the disaster from millions of Tweets is difficult.Different approaches have been developed to address this kind of problem.The recent state-of-the-art system was based on a manually created dictionary and this approach was further refined by introducing linguistic rules.However,these approaches suffer from limitations as they are dataset-dependent and not scalable.In this paper,we proposed a method to identify eyewitnesses from Twitter.To experiment,we utilized 13 features discovered by the pioneer of this domain and can classify the tweets to determine the eyewitness.Considering each feature,a dictionary of words was created with the Word Dictionary Maker algorithm,which is the crucial contribution of this research.This algorithm inputs some terms relevant to a specific feature for its initialization and then creates the words dictionary.Further,keyword matching for each feature in tweets is performed.If a feature exists in a tweet,it is termed as 1;otherwise,0.Similarly,for 13 features,we created a file that reflects features in each tweet.To classify the tweets based on features,Naïve Bayes,Random Forest,and Neural Network were utilized.The approach was implemented on different disasters like earthquakes,floods,hurricanes,and Forest fires.The results were compared with the state-of-the-art linguistic rule-based system with 0.81 F-measure values.At the same time,the proposed approach gained a 0.88 value of F-measure.The results were comparable as the proposed approach is not dataset-dependent.Therefore,it can be used for the identification of eyewitness accounts.
文摘The study evaluated the usefulness of repeat-interviewing of witnesses to crimes who were intoxicated by alcohol at the time of the incident and their first interview, and then re-interviewed when not intoxicated the following day. Sixty young, social drinkers were divided into three groups. One group was given a “placebo” (alcohol-like) beverage, a second was given a “low dose” of alcohol (0.2 g/kg men;0.17 g/kg women), and a third was given a “high dose” of alcohol (0.6 g/kg men;0.52 g/kg women) over a 15 minute period. Twenty minutes later they viewed a 4-minute video of a crime, and afterwards they were given two opportunities to recall everything that they could remember from the incident;the first opportunity was immediately after the event, and the second was 24 hours later. Analyses of the quantity and accuracy of the details recalled revealed no overall increase in the total amount of information recalled between the first and second recall opportunities. However, on average, 18% of the details recalled by participants in the second test were new and accurate. The incidence of contradictions between the first and second recall opportunities was less than 1%. Surprisingly, none of the effects were influenced by alcohol, even at the highest dose. The results imply that 1) memory for at least some incidents observed under the influence of alcohol is resilient even up to relatively high blood-alcohol levels;and 2) the repeated interviewing of witnesses who were intoxicated at the time of the crime can reveal additional, reliable information that is not present at the initial interview, just as is the case for non-intoxicated witnesses.
