We aim at solving the problem of predicting people's ideology, or political
tendency. We estimate it by using Twitter data, and formalize it as a
classification problem. Ideology-detection has long been a challenging yet
important problem. Certain groups, such as the policy makers, rely on it to
make wise decisions. Back in the old days when labor-intensive survey-studies
were needed to collect public opinions, analyzing ordinary citizens' political
tendencies was uneasy. The rise of social medias, such as Twitter, has enabled
us to gather ordinary citizen's data easily. However, the incompleteness of the
labels and the features in social network datasets is tricky, not to mention
the enormous data size and the heterogeneousity. The data differ dramatically
from many commonly-used datasets, thus brings unique challenges. In our work,
first we built our own datasets from Twitter. Next, we proposed TIMME, a
multi-task multi-relational embedding model, that works efficiently on
sparsely-labeled heterogeneous real-world dataset. It could also handle the
incompleteness of the input features. Experimental results showed that TIMME is
overall better than the state-of-the-art models for ideology detection on
Twitter. Our findings include: links can lead to good classification outcomes
without text; conservative voice is under-represented on Twitter; follow is the
most important relation to predict ideology; retweet and mention enhance a
higher chance of like, etc. Last but not least, TIMME could be extended to
other datasets and tasks in theory.

本篇文章主要研究如何利用多任务多关系嵌入模型（TIMME）处理 Twitter 数据集中的意识形态探测分类问题，文章指出 TIMME 相比其他模型有更好的综合表现。