GeoLocNN: An Efficient NN Approach for Accurate Tweet Geolocation Prediction
Keywords:
Twitter data collection, geo-tag, geo-location, Twitter location prediction, machine learning, deep learningAbstract
Twitter has emerged as the most popular social networking website where users can post their thoughts, opinions, life updates and many more things within a limited number of words which is up to 280 characters. If a user is performing some criminal activities like cyberbullying on such platforms, finding geolocations becomes important. In this article, we predict the geolocation of tweets posted in real time by using neural network techniques. The approach involves extracting features from the tweets and features associated with the tweets. The study introduces a novel deep-learning approach, GeoLocNN, for prediction of geo-location of tweet with higher accuracy. Using a blend of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, the approach outperforms traditional methods in precision and applicability. This provides significant implications for enhancing cybercrime analysis, leveraging spatial dynamics of social media data.
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