Difference between revisions of "Mark my words!"
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==summary == | ==summary == | ||
Physiological studies have suggested that participants in conversation accommodate in dimensions such as speaking style, utterance length, gesture, speaking rate etc. In this paper authors proves the hypothesis that linguistic accommodation could also be seen in social media such as Twitter. They investigate accommodation in LIWC dimensions[http://www.liwc.net/liwcdescription.php].Some of examples for these dimensions include use of Article,Negation words(not/no),Preposition,Quantifier,1st person singular pronoun,1st person plural pronoun,2nd person pronoun in conversation. Authors propose a novel probabilistic framework to prove their hypothesis. | Physiological studies have suggested that participants in conversation accommodate in dimensions such as speaking style, utterance length, gesture, speaking rate etc. In this paper authors proves the hypothesis that linguistic accommodation could also be seen in social media such as Twitter. They investigate accommodation in LIWC dimensions[http://www.liwc.net/liwcdescription.php].Some of examples for these dimensions include use of Article,Negation words(not/no),Preposition,Quantifier,1st person singular pronoun,1st person plural pronoun,2nd person pronoun in conversation. Authors propose a novel probabilistic framework to prove their hypothesis. | ||
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==Framework== | ==Framework== | ||
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Their framework is based on mainly two components, stylistic cohesion and stylistic accommodation. | Their framework is based on mainly two components, stylistic cohesion and stylistic accommodation. | ||
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'''Stylstic Cohesion''': It is used to find if tweets belonging to same conversation exhibit a certain LIWC style more than tweets which are unrelated. If the former is more then we can say that tweets which are part of same conversation agree more on a particular style. Formally, for a style <math> C </math> it is defined as: | '''Stylstic Cohesion''': It is used to find if tweets belonging to same conversation exhibit a certain LIWC style more than tweets which are unrelated. If the former is more then we can say that tweets which are part of same conversation agree more on a particular style. Formally, for a style <math> C </math> it is defined as: | ||
<math>Coh(C) =P(T^C \wedge R^C | T \leftrightarrow R)-P(T^C \wedge R^C)</math> | <math>Coh(C) =P(T^C \wedge R^C | T \leftrightarrow R)-P(T^C \wedge R^C)</math> | ||
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where <math> T \leftrightarrow R </math> is condition which represent that tweets represent a conversation turn. P(T^C \wedge R^C | T \leftrightarrow R) is the probability that tweets which are part of same conversation and exhibit the style C. Whereas, P(T^C \wedge R^C) represents that any randomly picked two tweets exhibit the style C. | where <math> T \leftrightarrow R </math> is condition which represent that tweets represent a conversation turn. P(T^C \wedge R^C | T \leftrightarrow R) is the probability that tweets which are part of same conversation and exhibit the style C. Whereas, P(T^C \wedge R^C) represents that any randomly picked two tweets exhibit the style C. | ||
Revision as of 00:23, 2 October 2012
summary
Physiological studies have suggested that participants in conversation accommodate in dimensions such as speaking style, utterance length, gesture, speaking rate etc. In this paper authors proves the hypothesis that linguistic accommodation could also be seen in social media such as Twitter. They investigate accommodation in LIWC dimensions[1].Some of examples for these dimensions include use of Article,Negation words(not/no),Preposition,Quantifier,1st person singular pronoun,1st person plural pronoun,2nd person pronoun in conversation. Authors propose a novel probabilistic framework to prove their hypothesis.
Framework
When individual talk about some topic, they would have to use similar words to describe topics hence it is important to remove topic accommodation from overall accommodation measure. Since they LIWC dimensions, it is automatically removed.
Their framework is based on mainly two components, stylistic cohesion and stylistic accommodation.
Stylstic Cohesion: It is used to find if tweets belonging to same conversation exhibit a certain LIWC style more than tweets which are unrelated. If the former is more then we can say that tweets which are part of same conversation agree more on a particular style. Formally, for a style it is defined as:
where is condition which represent that tweets represent a conversation turn. P(T^C \wedge R^C | T \leftrightarrow R) is the probability that tweets which are part of same conversation and exhibit the style C. Whereas, P(T^C \wedge R^C) represents that any randomly picked two tweets exhibit the style C.
