In this paper, we address the problem of detecting expressions of moral
values in tweets using content analysis. This is a particularly challenging
problem because moral values are often only implicitly signaled in language,
and tweets contain little contextual information due to length constraints. To
address these obstacles, we present a novel approach to automatically acquire
background knowledge from an external knowledge base to enrich input texts and
thus improve moral value prediction. By combining basic text features with
background knowledge, our overall context-aware framework achieves performance
comparable to a single human annotator. To the best of our knowledge, this is
the first attempt to incorporate background knowledge for the prediction of
implicit psychological variables in the area of computational social science.

本文中，我们提出了一个新的方法，结合基本的文本特征和外部知识库中获取的背景知识来检测推文中的道德价值表达，并在计算社会科学的领域中实现了与单个人注释员相媲美的性能。这是在预测隐含心理变量方面首次尝试使用背景知识。

获取背景知识以提高道德价值预测能力

Acquiring Background Knowledge to Improve Moral Value Prediction

A word's sentiment depends on the domain in which it is used. Computational
social science research thus requires sentiment lexicons that are specific to
the domains being studied. We combine domain-specific word embeddings with a
label propagation framework to induce accurate domain-specific sentiment
lexicons using small sets of seed words, achieving state-of-the-art performance
competitive with approaches that rely on hand-curated resources. Using our
framework we perform two large-scale empirical studies to quantify the extent
to which sentiment varies across time and between communities. We induce and
release historical sentiment lexicons for 150 years of English and
community-specific sentiment lexicons for 250 online communities from the
social media forum Reddit. The historical lexicons show that more than 5% of
sentiment-bearing (non-neutral) English words completely switched polarity
during the last 150 years, and the community-specific lexicons highlight how
sentiment varies drastically between different communities.

通过使用领域特定的词向量和标签传播框架，结合少量种子单词，创造与手动资源相媲美的精准的领域特定情感词典，用于量化情感在时间和社区之间的差异，并在此基础上，提出了历史情感词汇表和社区特定情感词汇表。

从未标记的语料库中诱导特定领域情感词典

Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora

Use of socially generated "big data" to access information about collective
states of the minds in human societies has become a new paradigm in the
emerging field of computational social science. A natural application of this
would be the prediction of the society's reaction to a new product in the sense
of popularity and adoption rate. However, bridging the gap between "real time
monitoring" and "early predicting" remains a big challenge. Here we report on
an endeavor to build a minimalistic predictive model for the financial success
of movies based on collective activity data of online users. We show that the
popularity of a movie can be predicted much before its release by measuring and
analyzing the activity level of editors and viewers of the corresponding entry
to the movie in Wikipedia, the well-known online encyclopedia.

使用社交媒体生成的 "大数据"，预测电影的财务成功，其中包括测量和分析维基百科上与电影对应的条目的编辑和观看者的活动水平。