User profile learning, such as mobility and demographic profile learning, is of great importance to various applications. Meanwhile, the rapid growth of multiple social platforms makes it possible to perform a comprehensive user profile learning from different views. However, the research efforts on user profile learning from multiple data sources are still relatively sparse, and there is no large-scale dataset released towards user profile learning. In our study, we contribute such benchmark and perform an initial study on user mobility and demographic profile learning. First, we constructed and released a large-scale multi-source multimodal dataset from three geographical areas. We then applied our proposed ensemble model on this dataset to learn user profile. Based on our experimental results, we observed that multiple data sources mutually complement each other and their appropriate fusion boosts the user profiling performance.
In this technical demonstration, we showcase the first ai-driven social multimedia influencer discovery marketplace, called SoMin. The platform combines advanced data analytics and behavioral science to help marketers find, understand their audience and engage the most relevant social media micro-influencers at a large scale. SoMin harvests brand-specific life social multimedia streams in a specified market domain, followed by rich analytics and semantic-based influencer search. The Individual User Profiling models extrapolate the key personal characteristics of the brand audience, while the influencer retrieval engine reveals the semantically-matching social media influencers to the platform users. The influencers are matched in terms of both their-posted content and social media audiences, while the evaluation results demonstrate an excellent performance of the proposed recommender framework. By leveraging influencers at a large scale, marketers will be able to execute more effective marketing campaigns of higher trust and at a lower cost.
The exponential growth of online social networks has inspired us to tackle the problem of individual user attributes inference from the Big Data perspective. It is well known that various social media networks exhibit different aspects of user interactions, and thus represent users from diverse points of view. In this preliminary study, we make the first step towards solving the significant problem of personality profiling from multiple social networks. Specifically, we tackle the task of relationship prediction, which is closely related to our desired problem. Experimental results show that the incorporation of multi-source data helps to achieve better prediction performance as compared to single-source baselines.
President Trump’s Twitter habits fooled Russian scientists into thinking he was a single guy, according to a new study.
Researchers from Information Technology, Optical Design and Engineering University in Saint Petersburg used artificial intelligence trained to determine a person’s marital status based on their social media use on tweets by Trump and former President Obama.
President Donald Trump’s erratic Twitter habits led a sophisticated computer algorithm to conclude that he lives “like a bachelor”.
The artificial intelligence system, which is used to predict the marital status of social media accounts and claims to have an 86 per cent accuracy rate, identified Mr Trump as “not married”.
AI trained to predict user marital status by analyzing three social media sites
It is capable of predicting marital status with 86 percent precision
Found Donald Trump tweets like a bachelor and identified him as not-married
Experts say it is because Trump's assistants will also update his Twitter account