Weakly Supervised Tweet Stance Classification by Relational Bootstrapping

Abstract

Supervised stance classification, in such domains as Congressional debates and online forums, has been a topic of interest in the past decade. Approaches have evolved from text classification to structured output prediction, including collective classification and sequence labeling. In this work, we investigate collective classification of stances on Twitter, using hinge-loss Markov random fields (HL-MRFs). Given the graph of all posts, users, and their relationships, we constrain the predicted post labels and latent user labels to correspond with the network structure. We focus on a weakly supervised setting, in which only a small set of hashtags or phrases is labeled. Using our relational approach, we are able to go beyond the stance-indicative patterns and harvest more stance-indicative tweets, which can also be used to train any linear text classifier when the network structure is not available or is costly.

Leveraging Behavioral and Social Information for Weakly Supervised Collective Classification of Political Discourse on Twitter

“…Analyzing political tweets has also attracted considerable interest: a recent SemEval task looked into stance prediction, 2 and more related to our work, Tan et al (2014) have shown how wording choices can affect message propagation on Twitter. Two recent works look into predicting stance (at user and tweet levels respectively) on Twitter using PSL (Johnson and Goldwasser, 2016;Ebrahimi et al, 2016). Frame classification, however, has a finer granularity than stance classification and describes how someone expresses their view on an issue, not whether they support the issue.…”

The evolution of argumentation mining: From models to social media and emerging tools

“…Algorithm Metric Score Zarella and Marsh [69] Favor/against/neither RNN F1 0.68 Mohammad et al [68] Favor/against/neither linear-kernel SVM F-micro 0.70 F-macro 0.59 Wei et al [98] Favor/against/neither Neural network F1 0.56 Wei et al [70] Favor/against Neural network F1 0.71 Ebrahimi et al [71] Favor/against/neither Linear-kernel SVM F macro 0.57 Johnson and Goldwasser [73] Favor/against Probabilistic Soft Logic A 0.86 Lai et al [72] Favor/against SVM F-macro 0.90 Addawood et al [60] Favor/against/neutral SVM P 0.90 R 0.90 F1 0.90 Table 6: The results of the supervised and weakly-supervised ML approaches that have been followed for the stance detection in social media text. Fully supervision on [69], [68], [60].…”

“…Fully supervision on [69], [68], [60]. Weakly supervision on [70], [71], [73], [72]. The [69], [68], [98], [71] and [60] are applied on the same dataset.…”

“…Weakly supervision on [70], [71], [73], [72]. The [69], [68], [98], [71] and [60] are applied on the same dataset. RNN = Recurrent Neural Network where h i is the person who holds a specific stance s ijkl for a specific debate d l , justified by a rationale r k in a specific time t l .…”

Bridging Weighted Rules and Graph Random Walks for Statistical Relational Models

“…Statistical relational learning (Getoor and Taskar, 2007), or statistical relational AI (StarAI) (De Raedt et al, 2016), aims at probabilistic reasoning and learning when there are (possibly various types of) relationships among the objects. The relational models developed in StarAI community have been successfully applied to several applications such as knowledge graph completion (Lao et al, 2011;Nickel et al, 2012;Bordes et al, 2013;Pujara et al, 2013;Trouillon et al, 2016), entity resolution (Singla and Domingos, 2006;Bhattacharya and Getoor, 2007;Pujara and Getoor, 2016;Fatemi, 2017), tasks in scientific literature (Lao and Cohen, 2010b), stance classification (Sridhar et al, 2015;Ebrahimi et al, 2016), question answering (Khot et al, 2015;Dries et al, 2017), etc.…”