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Finding Authentic Counterhate Arguments: A Case Study with Public Figures
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A substantial amount of evidence indicates that news coverage of suicide deaths by celebrities is followed by an increase in suicide rates, suggesting a copycat behavior. However, the underlying process by which celebrity status and media coverage leads to increases in subsequent suicides is still unclear. This study collected over 1 million individual messages (“tweets”) posted on Twitter that were related to 26 prominent figures in Japan who died by suicide between 2010 and 2014 and investigated whether media reports on suicide deaths that generated a greater level of reactions by the public are likely to be followed by a larger increase in actual suicides. We also compared the number of Twitter posts and the number of media reports in newspaper and on television to understand whether the number of messages on Twitter in response to the deaths corresponds to the amount of coverage in the traditional media. Using daily data from Japan's national death registry between 2010 and 2014, our analysis found an increase in actual suicides only when suicide deaths generated a large reaction from Twitter users. In contrast, no discernible increase in suicide counts was observed when the analysis included suicide deaths to which Twitter users did not show much interest, even when these deaths were covered considerably by the traditional media. This study also found suicides by relatively young entertainers generated a large number of posts on Twitter. This sharply contrasts with the relatively smaller volume of reaction to them generated by traditional forms of media, which focuses more on the deaths of non-entertainers. The results of this study strongly suggest that it is not sufficient to examine only traditional news media when investigating the impact of media reports on actual suicides.
This article is a survey of methods for measuring agreement among corpus annotators. It exposes the mathematics and underlying assumptions of agreement coefficients, covering Krippendorff's alpha as well as Scott's pi and Cohen's kappa; discusses the use of coefficients in several annotation tasks; and argues that weighted, alpha-like coefficients, traditionally less used than kappa-like measures in computational linguistics, may be more appropriate for many corpus annotation tasks—but that their use makes the interpretation of the value of the coefficient even harder.
We present an approach to detecting hate speech in online text, where hate speech is defined as abusive speech targeting specific group characteristics, such as ethnic origin, religion, gender, or sexual orientation. While hate speech against any group may exhibit some common characteristics, we have observed that hatred against each different group is typically characterized by the use of a small set of high frequency stereotypical words; however, such words may be used in either a positive or a negative sense, making our task similar to that of words sense disambiguation. In this paper we describe our definition of hate speech, the collection and annotation of our hate speech corpus, and a mechanism for detecting some commonly used methods of evading common "dirty word" filters. We describe pilot classification experiments in which we classify anti-semitic speech reaching an accuracy 94%, precision of 68% and recall at 60%, for an F1 measure of. 6375.
Even though considerable attention has been given to the polarity of words
(positive and negative) and the creation of large polarity lexicons, research
in emotion analysis has had to rely on limited and small emotion lexicons. In
this paper we show how the combined strength and wisdom of the crowds can be
used to generate a large, high-quality, word-emotion and word-polarity
association lexicon quickly and inexpensively. We enumerate the challenges in
emotion annotation in a crowdsourcing scenario and propose solutions to address
them. Most notably, in addition to questions about emotions associated with
terms, we show how the inclusion of a word choice question can discourage
malicious data entry, help identify instances where the annotator may not be
familiar with the target term (allowing us to reject such annotations), and
help obtain annotations at sense level (rather than at word level). We
conducted experiments on how to formulate the emotion-annotation questions, and
show that asking if a term is associated with an emotion leads to markedly
higher inter-annotator agreement than that obtained by asking if a term evokes
an emotion.
Twitter is a major forum for rapid dissemination of user-provided content in real time. As such, a large proportion of the information it contains is not particularly relevant to many users and in fact is perceived as unwanted 'noise' by many. There has been increased research interest in predicting whether tweets are relevant, newsworthy or credible, using a variety of models and methods. In this paper, we focus on an analysis that highlights the utility of the individual features in Twitter such as hash tags, retweets and mentions for predicting credibility. We first describe a context-based evaluation of the utility of a set of features for predicting manually provided credibility assessments on a corpus of microblog tweets. This is followed by an evaluation of the distribution/presence of each feature across 8 diverse crawls of tweet data. Last, an analysis of feature distribution across dyadic pairs of tweets and retweet chains of various lengths is described. Our results show that the best indicators of credibility include URLs, mentions, retweets and tweet length and that features occur more prominently in data describing emergency and unrest situations.
The scientific study of hate speech, from a computer science point of view, is recent. This survey organizes and describes the current state of the field, providing a structured overview of previous approaches, including core algorithms, methods, and main features used. This work also discusses the complexity of the concept of hate speech, defined in many platforms and contexts, and provides a unifying definition. This area has an unquestionable potential for societal impact, particularly in online communities and digital media platforms. The development and systematization of shared resources, such as guidelines, annotated datasets in multiple languages, and algorithms, is a crucial step in advancing the automatic detection of hate speech.
Detection of abusive language in user generated online content has become an issue of increasing importance in recent years. Most current commercial methods make use of blacklists and regular expressions, however these measures fall short when contending with more subtle, less ham-fisted examples of hate speech. In this work, we develop a machine learning based method to detect hate speech on online user comments from two domains which outperforms a state-of-the-art deep learning approach. We also develop a corpus of user comments annotated for abusive language, the first of its kind. Finally, we use our detection tool to analyze abusive language over time and in different settings to further enhance our knowledge of this behavior.
Twitter is now used to distribute substantive content such as breaking news, increasing the importance of assessing the credibility of tweets. As users increasingly access tweets through search, they have less information on which to base credibility judgments as compared to consuming content from direct social network connections. We present survey results regarding users' perceptions of tweet credibility. We find a disparity between features users consider relevant to credibility assessment and those currently revealed by search engines. We then conducted two experiments in which we systematically manipulated several features of tweets to assess their impact on credibility ratings. We show that users are poor judges of truthfulness based on content alone, and instead are influenced by heuristics such as user name when making credibility assessments. Based on these findings, we discuss strategies tweet authors can use to enhance their credibility with readers (and strategies astute readers should be aware of!). We propose design improvements for displaying social search results so as to better convey credibility.
Benchmarking aggression identification in social media
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Hatexplain: A benchmark dataset for explainable hate speech detection
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Pytorch: An imperative style, high-performance deep learning library
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- Nn
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Pattern
#
Pattern
1
[individual's name, is, 'JJ']
2
[individual's name, is, 'JJ', 'NN']
3
[individual's name, is, 'NN']
4
[individual's name, is, 'NN', 'NN']
5
[individual's name, is, 'DT', 'JJ']
6
[individual's name, is, 'DT', 'JJ', 'NN']
7
[individual's name, is, 'DT', 'JJ', 'NN', 'NN']
8
[individual's name, is, 'DT', 'JJS']
9
[individual's name, is, 'DT', 'JJS', 'NN']
10 [individual's name, is, 'DT', 'NN']
11 [individual's name, is, 'DT', 'NN', 'NN']
12 [individual's name, is, 'DT', 'NN', 'IN', 'NN']
13 [individual's name, is, 'DT', 'NNP']
14 [individual's name, is, 'DT', 'NNP', 'VBG']
15 [individual's name, is, 'DT', 'VBG', 'NN']
16 [individual's name, is, 'DT', 'RBS', 'JJ']
17 [individual's name, is, 'DT', 'RBS', 'VBN']
18 [individual's name, is, 'PDT', 'DT', 'JJ']