Summary 1:
The authors try to identify politeness by analyzing texts in purpose of analyzing how politeness in language can effect various social factors as well as people. They analyze politeness factors from texts and comments in different aspects using Wikipedia and Stackexchange data. They also build an automated classifier to extract politeness score from texts. Their observation finds out the variation of politeness score based on gender, demography as well as status. The politeness data collected would be insightful to study different aspects of social and political factors.
Reflection 1:
The paper presents at first presents a linguistic analysis of how politeness varies in different words used in different sentences. Also, a same word can have different politeness scores for different use of sentences. They collected data from Wikipedia and Stackexchange and use human efforts to annotate those data with politeness scores. This data has been used to build two SVM classifiers- one using unigram feature representation another using threshold value of a politeness score for unigram features. Although, the classifier has high accuracy rate of predicting a word being ‘polite’ and ‘impolite’, my point is will this data work for all aspects other than Wikipedia and Stackexchange. Suppose, a word might have high politeness score in these sites but they can be used as satirical or for other negative expression in social networks. Also, here they used unigram feature which is very weak. Instead of this they could also bring context words or n-gram features to analyze and build the classifier.
Summary 2:
In this paper the authors study on the racial disparities shown by the police officers by analyzing their linguistic interaction with people. This study was conducted in three steps- First, perceiving the behavior of police officers from their language, second, identifying correlations between sentences or words with respect and finally, to find out the presence of racial disparity in an officer by correlating other studies. The data collected for this study have been collected by transcribing video footage of officers at stop points into texts. The experiment finds out that most officers are likely to treat white people with more respect than black and people of other races.
Reflection 2:
The idea of the paper was to find out the behavior of each individual police officer at from the ratings given by participants for them. Then this computes the feature of each utterances of a police officer. Figure 2 shows a statistical graph of use of each feature for the white and black people along with the respect coefficient score of each feature. They claim that their analysis correlates with the human study obtained from the participants. However, it did not study about the officer mood, work load and other factors which might influence a officer’s behavior.
