Stance detection aims to automatically identify if the text author is in favor or against a subject or target. This work describes a semi-supervised method for stance detection. The core is a set of rules to identify stance based on positive or negative opinions of targets directly or indirectly related. Tweets automatically labeled using the rules compose a training corpus for a supervised app...

In this paper, we propose a novel graph-based method for semi-supervised learning. Our method runs a diffusion-based affinity learning algorithm on an augmented graph consisting of not only the nodes of labeled and unlabeled data but also artificial nodes representing class labels. The learned affinities between unlabeled data and class labels are used for classification. Our method achieves su...

Conventional graph-based semi-supervised learning methods predominantly focus on single label problem. However, it is more popular in real-world applications that an example is associated with multiple labels simultaneously. In this paper, we propose a novel graph-based learning framework in the setting of semi-supervised learning with multiple labels. This framework is characterized by simulta...

We present two techniques to improve landmark localization in images from partially annotated datasets. Our primary goal is to leverage the common situation where precise landmark locations are only provided for a small data subset, but where class labels for classification or regression tasks related to the landmarks are more abundantly available. First, we propose the framework of sequential ...

The harmonic solution (HS) on a graph is one of the most popular approaches to semi-supervised learning. This is the first paper that studies how to identify highly confident HS predictions on a graph based on the HS on its subgraph. The premise of our method is that the subgraph is much smaller than the graph and therefore the most confident predictions can be identified much faster than compu...

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Emotion classification can be generally done from both the writer’s and reader’s perspectives. In this study, we find that two foundational tasks in emotion classification, i.e., reader’s emotion classification on the news and writer’s emotion classification on the comments, are strongly related to each other in terms of coarse-grained emotion categories, i.e., negative and positive. On the bas...

The participating approach predicts whether a tweet, which is accompanied by multimedia content (image/video), is trustworthy (real) or deceptive (fake). We combine two different methods a) one using a semi-supervised learning scheme that leverages the decisions of two independent classifiers to produce a decision and b) one using textual patterns to extract claims about whether a post is fake ...

In this paper, we present a new analysis on co-training, a representative paradigm of disagreement-based semi-supervised learning methods. In our analysis the co-training process is viewed as a combinative label propagation over two views; this provides a possibility to bring the graph-based and disagreementbased semi-supervised methods into a unified framework. With the analysis we get some in...

Speech transcripts often only capture semantic content, omitting disfluencies that can be useful for analyzing social dynamics of a discussion. This work describes steps in building a model that can recover a large fraction of locations where disfluencies were present, by transforming carefully annotated text to match the standard transcription style, introducing a two-stage model for handling ...