We propose a location prediction method for tweets based on the geographical probability distribution of their terms over a region. In our method, the probabilities are calculated using Kernel Density Estimation (KDE), where the bandwidth of the kernel function for each term is determined separately according to the location indicativeness of the term. Prediction for a new tweet is performed by combining the probability distributions of its terms weighted by their information gain ratio. The method we propose relies on statistical approaches without requiring any parameter tuning. Experiments conducted on three tweet sets from different regions of the world indicate significant improvement in prediction accuracy compared to the state-of-the-art methods.

This paper aims to enhance event detection methods in a micro-blogging platform, namely Twitter. The enhancement technique we propose is based on lexico-semantic expansion of tweet contents while applying document similarity and clustering algorithms. Considering the length limitations and idiosyncratic spelling in Twitter environment, it is possible to take advantage of word similarities and to enrich texts with similar words. The semantic expansion technique we implement is based on syntagmatic and paradigmatic relationships between words, extracted from their co-occurrence statistics. As our technique does not depend on an existing ontology or a lexicon database such as Word Net, it should be applicable for any language. The proposed technique is applied on a tweet set collected for three days from the users in Turkey. The results indicate earlier detection of events and improvements in accuracy.