A Solution to Tweet-Based User Identification Across Online Social Networks.

User identification can help us build better users' profiles and benefit many applications. It has attracted many scholars' attention. The existing works with good performance are mainly based on the rich online data. However, due to the privacy settings, it is costless or even difficult to obtain the rich data. Besides some profile attributes do not require exclusivity and are easily faked by users for different purposes. This makes the existing schemes are quite fragile. Users often publicly publish their activities on different social networks. This provides a way to overcome the above problem. We aim to address the user identification only based on users' tweets. We first formulate the user identification based on tweets and propose a tweet-based user identification model. Then a supervised machine learning based solution is presented. It consists of three key steps: first, we propose several algorithms to measure the spatial similarity, temporal similarity and content similarity of two tweets; second, we extract the spatial, temporal and content features to exploit information redundancies; Afterwards, we employ the machine learning method for user identification. The experiment shows that the proposed solution can provide excellent performance with F1 values reaching 89.79%, 86.78% and 86.24% on three ground truth datasets, respectively. This work shows the possibility of user identification with easily accessible and not easily impersonated online data.