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。

TIMME: 多任务多关系嵌入实现 Twitter 意识形态检测

TIMME: Twitter Ideology-detection via Multi-task Multi-relational  Embedding

The problem of ideology detection is to study the latent (political)
placement for people, which is traditionally studied on politicians according
to their voting behaviors. Recently, more and more studies begin to address the
ideology detection problem for ordinary users based on their online behaviors
that can be captured by social media, e.g., Twitter. As far as we are
concerned, however, the vast majority of the existing methods on ideology
detection on social media have oversimplified the problem as a binary
classification problem (i.e., liberal vs. conservative). Moreover, though
social links can play a critical role in deciding one's ideology, most of the
existing work ignores the heterogeneous types of links in social media. In this
paper we propose to detect \emph{numerical} ideology positions for Twitter
users, according to their \emph{follow}, \emph{mention}, and \emph{retweet}
links to a selected set of politicians. A unified probabilistic model is
proposed that can (1) explain the reasons why links are built among people in
terms of their ideology, (2) integrate heterogeneous types of links together in
determining people's ideology, and (3) automatically learn the quality of each
type of links in deciding one's ideology. Experiments have demonstrated the
advantages of our model in terms of both ranking and political leaning
classification accuracy. It is shown that (1) using multiple types of links is
better than using any single type of links alone to determine one's ideology,
and (2) our model is even more superior than baselines when dealing with people
that are sparsely linked in one type of links. We also show that the detected
ideology for Twitter users aligns with our intuition quite well.

本文提出了一种新的基于社交媒体信息的意识形态检测方法，利用 Twitter 用户与政治人物的关注、提到和转发链接来确定用户的意识形态，使用概率模型解释了链接建立的原因，并自动学习了每种类型链接对决定一个用户意识形态的重要性，实验表明该模型在排名和政治取向分类精度方面具有优势，与直觉相符。