Mining the Demographics of Political Sentiment from Twitter Using Learning from Label Proportions

Opinion mining and demographic attribute inference have many applications in social science. In this paper, we propose models to infer daily joint probabilities of multiple latent attributes from Twitter data, such as political sentiment and demographic attributes. Since it is costly and time-consuming to annotate data for traditional supervised classification, we instead propose scalable Learning from Label Proportions (LLP) models for demographic and opinion inference using U.S. Census, national and state political polls, and Cook partisan voting index as population level data. In LLP classification settings, the training data is divided into a set of unlabeled bags, where only the label distribution in of each bag is known, removing the requirement of instance-level annotations. Our proposed LLP model, Weighted Label Regularization (WLR), provides a scalable generalization of prior work on label regularization to support weights for samples inside bags, which is applicable in this setting where bags are arranged hierarchically (e.g., county-level bags are nested inside of state-level bags). We apply our model to Twitter data collected in the year leading up to the 2016 U.S. presidential election, producing estimates of the relationships among political sentiment and demographics over time and place. We find that our approach closely tracks traditional polling data stratified by demographic category, resulting in error reductions of 28-44% over baseline approaches. We also provide descriptive evaluations showing how the model may be used to estimate interactions among many variables and to identify linguistic temporal variation, capabilities which are typically not feasible using traditional polling methods

Incremental learning with social media data to predict near real-time events

International audienceIn this paper, we focus on the problem of predicting some particular user activities in social media. Our challenge is to consider real events such as message posting to friends or forwarding received ones, connecting to new friends, and provide near real-time prediction of new events. Our approach is based on latent factor models which can exploit simultaneously the timestamped interaction information among users and their posted content information. We propose a simple strategy to learn incrementally the latent factors at each time step. Our method takes only recent data to update latent factor models and thus can reduce computational cost. Experiments on a real dataset collected from Twitter show that our method can achieve performances that are comparable with other state-of-the-art non-incremental techniques

A Study on User Demographic Inference Via Ratings in Recommender Systems

Everyday, millions of people interact with online services that adopt recommender systems, such as personalized movie, news and product recommendation services. Research has shown that the demographic attributes of users such as age and gender can further improve the performance of recommender systems and can be very useful for many other applications such as marketing and social studies. However, users do not always provide those details in their online profiles due to privacy concern. On the other hand, user interactions such as ratings in recommender systems may provide an alternative way to infer demographic information. Most existing approaches can infer user demographics based on sufficient interaction history but could fail for users with few ratings. In this thesis, we study the association between users demographic information and their ratings, and explore the tradeoff between user privacy and the utility of personalization. In particular, we present a novel multi-task preference elicitation method, with which a recommender system asks a new user to rate selected items adaptively and infers the demographics rapidly via a few interactions. Experimental results on real-world datasets demonstrate the performance of the proposed method in terms of the accuracy of both demographics inference and rating prediction