3,850 research outputs found
Towards Measuring Adversarial Twitter Interactions against Candidates in the US Midterm Elections
Adversarial interactions against politicians on social media such as Twitter
have significant impact on society. In particular they disrupt substantive
political discussions online, and may discourage people from seeking public
office. In this study, we measure the adversarial interactions against
candidates for the US House of Representatives during the run-up to the 2018 US
general election. We gather a new dataset consisting of 1.7 million tweets
involving candidates, one of the largest corpora focusing on political
discourse. We then develop a new technique for detecting tweets with toxic
content that are directed at any specific candidate.Such technique allows us to
more accurately quantify adversarial interactions towards political candidates.
Further, we introduce an algorithm to induce candidate-specific adversarial
terms to capture more nuanced adversarial interactions that previous techniques
may not consider toxic. Finally, we use these techniques to outline the breadth
of adversarial interactions seen in the election, including offensive
name-calling, threats of violence, posting discrediting information, attacks on
identity, and adversarial message repetition
Comparative Studies of Detecting Abusive Language on Twitter
The context-dependent nature of online aggression makes annotating large
collections of data extremely difficult. Previously studied datasets in abusive
language detection have been insufficient in size to efficiently train deep
learning models. Recently, Hate and Abusive Speech on Twitter, a dataset much
greater in size and reliability, has been released. However, this dataset has
not been comprehensively studied to its potential. In this paper, we conduct
the first comparative study of various learning models on Hate and Abusive
Speech on Twitter, and discuss the possibility of using additional features and
context data for improvements. Experimental results show that bidirectional GRU
networks trained on word-level features, with Latent Topic Clustering modules,
is the most accurate model scoring 0.805 F1.Comment: ALW2: 2nd Workshop on Abusive Language Online to be held at EMNLP
2018 (Brussels, Belgium), October 31st, 201
Deep Learning for User Comment Moderation
Experimenting with a new dataset of 1.6M user comments from a Greek news
portal and existing datasets of English Wikipedia comments, we show that an RNN
outperforms the previous state of the art in moderation. A deep,
classification-specific attention mechanism improves further the overall
performance of the RNN. We also compare against a CNN and a word-list baseline,
considering both fully automatic and semi-automatic moderation
Extractive Adversarial Networks: High-Recall Explanations for Identifying Personal Attacks in Social Media Posts
We introduce an adversarial method for producing high-recall explanations of
neural text classifier decisions. Building on an existing architecture for
extractive explanations via hard attention, we add an adversarial layer which
scans the residual of the attention for remaining predictive signal. Motivated
by the important domain of detecting personal attacks in social media comments,
we additionally demonstrate the importance of manually setting a semantically
appropriate `default' behavior for the model by explicitly manipulating its
bias term. We develop a validation set of human-annotated personal attacks to
evaluate the impact of these changes.Comment: Accepted to EMNLP 2018 Code and data available at
https://github.com/shcarton/rcn
The Impact of Online Harassment on the Performance of Projects in Crowdfunding
In the consequence-free and anonymous online environment, online harassment has become a serious problem. In many crowdfunding platforms, there exists offensive speech on the project pages, which might force potential funders to leave the discussion and to give up investment. The effect of online harassment on project performance remains unknown. This study attempts to investigate to what extent the textual online harassment score and the project creator’s attitude towards textual online harassment might affect project performance. We constructed a Kickstarter panel dataset consisting of 388,100 projects and designed a novel framework and an algorithm BiLSTM-CNN to extract the textual online harassment score from comments, which can reach column-wise mean ROC AUC of 0.9463. This study contributes to crowdfunding and online harassment literature and provides important implications for reputation management of projects and crowdfunding platform design
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