Estimating Twitter User Location Using Social Interactions

Estimating Twitter User Location Using Social Interactions

Estimating Twitter User Location Using Social Interactions management report in python .Microblogging services such as Twitter allow users to interact with each other by forming a social network. The interaction between users in a social network group forms a dialogue or discussion. A typical dialogue between users involves a set of topics. We make the assumption that this set of topics remains constant throughout the conversation. Using this model of social interaction between users in the Twitter social network, along with content-derived location information, we employ a probabilistic framework to estimate the city-level location of a Twitter user, based on the content of the tweets in their dialogues, using reply-tweet messages. Download Best Python Project on Estimating Twitter User Location Using Social . Click here to get complete Python projects lists.

We estimate the city-level user location based purely on the content of the tweets, which may include reply-tweet information, without the use of any external information, such as a gazetteer, IP information etc. The current framework for estimating user location does not consider the underlying social interaction, i.e. the structure of interactions between the users. In this paper, we calculate a baseline probability estimate of the distribution of words used by a user. This distribution is formed by using the fact that terms used in the tweets of a certain discussion may be related to the location information of the user initiating the discussion. We also estimate the top K probable cities for a given user and measure the accuracy. We find that our baseline estimation yields an accuracy higher that the 10% accuracy of the current state of the art estimation.

Conclusion

Estimating Twitter User Location Using Social Interactions management report in python. The experiment performed in this paper provides good insight into the problem of estimating a user’s geographic location information purely based on the content of the user’s publicly available information, while making use of the characteristics of the Twitter communication model. We use the concept of user interactions on Twitter and exploit the relationship between of different tweet message types. From the experimental results, we conclude that associating the tweet content of a conversation, containing reply-tweets, with the initial tweet’s user’s location (to obtain a spatial distribution of terms), improves the accuracy of estimating a user location.

Further, the quality of this work can be refined by considering a larger data set that takes into account the reply messages for a given tweet. We would also like to see further improvements by combining the information in the underlying social network with additional information, to obtain a more accurate prediction of user location in a social network environment. Download Best Python Project on Estimating Twitter User Location Using Social .

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