Detecting and labeling stance in social media text is strongly motivated by
hate speech detection, poll prediction, engagement forecasting, and concerted
propaganda detection. Today's best neural stance detectors need large volumes
of training data, which is difficult to curate given the fast-changing
landscape of social media text and issues on which users opine. Homophily
properties over the social network provide strong signal of coarse-grained
user-level stance. But semi-supervised approaches for tweet-level stance
detection fail to properly leverage homophily. In light of this, We present
SANDS, a new semi-supervised stance detector. SANDS starts from very few
labeled tweets. It builds multiple deep feature views of tweets. It also uses a
distant supervision signal from the social network to provide a surrogate loss
signal to the component learners. We prepare two new tweet datasets comprising
over 236,000 politically tinted tweets from two demographics (US and India)
posted by over 87,000 users, their follower-followee graph, and over 8,000
tweets annotated by linguists. SANDS achieves a macro-F1 score of 0.55 (0.49)
on US (India)-based datasets, outperforming 17 baselines (including variants of
SANDS) substantially, particularly for minority stance labels and noisy text.
Numerous ablation experiments on SANDS disentangle the dynamics of textual and
network-propagated stance signals.

本研究介绍了一种新的基于半监督学习的立场检测方法 SANDS，通过构建深度特征视图和利用社交网络提供的远程监督信号，SANDS 在美国和印度两个不同政治派别下的推文数据上获得了较高的 F1 得分 0.55 和 0.49。

利用远程网络监督进行半监督推文态度检测

Semi-supervised Stance Detection of Tweets Via Distant Network Supervision

We report results of a comparison of the accuracy of crowdworkers and seven
Natural Language Processing (NLP) toolkits in solving two important NLP tasks,
named-entity recognition (NER) and entity-level sentiment (ELS) analysis. We
here focus on a challenging dataset, 1,000 political tweets that were collected
during the U.S. presidential primary election in February 2016. Each tweet
refers to at least one of four presidential candidates, i.e., four named
entities. The groundtruth, established by experts in political communication,
has entity-level sentiment information for each candidate mentioned in the
tweet. We tested several commercial and open-source tools. Our experiments show
that, for our dataset of political tweets, the most accurate NER system, Google
Cloud NL, performed almost on par with crowdworkers, but the most accurate ELS
analysis system, TensiStrength, did not match the accuracy of crowdworkers by a
large margin of more than 30 percent points.

比较了众包工作者和七个自然语言处理工具在命名实体识别和实体级情感分析两个重要 NLP 任务中的准确性，并测试了几个商业和开源工具，实验结果表明，在我们的政治推文数据集中，最准确的 NER 系统谷歌云 NL 表现几乎与众包工作者一样，但最准确的 ELS 分析系统 TensiStrength 的准确性与众包工作者的准确性相差很大，超过 30 个百分点。

众包工作者与自然语言处理工具在政治推文的命名实体识别和情感分析方面的性能比较

Performance Comparison of Crowdworkers and NLP Tools on Named-Entity  Recognition and Sentiment Analysis of Political Tweets

Opinions about the 2016 U.S. Presidential Candidates have been expressed in
millions of tweets that are challenging to analyze automatically. Crowdsourcing
the analysis of political tweets effectively is also difficult, due to large
inter-rater disagreements when sarcasm is involved. Each tweet is typically
analyzed by a fixed number of workers and majority voting. We here propose a
crowdsourcing framework that instead uses a dynamic allocation of the number of
workers. We explore two dynamic-allocation methods: (1) The number of workers
queried to label a tweet is computed offline based on the predicted difficulty
of discerning the sentiment of a particular tweet. (2) The number of crowd
workers is determined online, during an iterative crowd sourcing process, based
on inter-rater agreements between labels.We applied our approach to 1,000
twitter messages about the four U.S. presidential candidates Clinton, Cruz,
Sanders, and Trump, collected during February 2016. We implemented the two
proposed methods using decision trees that allocate more crowd efforts to
tweets predicted to be sarcastic. We show that our framework outperforms the
traditional static allocation scheme. It collects opinion labels from the crowd
at a much lower cost while maintaining labeling accuracy.

本研究提出了一种基于动态分配众包的方法，以更低的成本从 Twitter 上收集关于美国总统候选人的意见标签，并通过决策树来分配更多的用户努力预测的具有讽刺意味的标题，从而提高标注精度。