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Register a new userWho has the last word? Understanding How to Sample Online Discussions
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Added by
Gioia Boschi
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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In online debates individual arguments support or attack each other, leading to some subset of arguments being considered more relevant than others. However, in large discussions readers are often forced to sample a subset of the arguments being put forth. Since such sampling is rarely done in a principled manner, users may not read all the relevant arguments to get a full picture of the debate. This paper is interested in answering the question of how users should sample online conversations to selectively favour the currently justified or accepted positions in the debate. We apply techniques from argumentation theory and complex networks to build a model that predicts the probabilities of the normatively justified arguments given their location in online discussions. Our model shows that the proportion of replies that are supportive, the number of replies that comments receive, and the locations of un-replied comments all determine the probability that a comment is a justified argument. We show that when the degree distribution of the number of replies is homogeneous along the discussion, for acrimonious discussions, the distribution of justified arguments depends on the parity of the graph level. In supportive discussions the probability of having justified comments increases as one moves away from the root. For discussion trees that have a non-homogeneous in-degree distribution, for supportive discussions we observe the same behaviour as before, while for acrimonious discussions we cannot observe the same parity-based distribution. This is verified with data obtained from the online debating platform Kialo. By predicting the locations of the justified arguments in reply trees, we can suggest which arguments readers should sample to grasp the currently accepted opinions in such discussions. Our models have important implications for the design of future online debating platforms.
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Qualitative research provides methodological guidelines for observing and studying communities and cultures on online social media platforms. However, such methods demand considerable manual effort from researchers and may be overly focused and narrowed to certain online groups. In this work, we propose a complete solution to accelerate qualitative analysis of problematic online speech -- with a specific focus on opinions emerging from online communities -- by leveraging machine learning algorithms. First, we employ qualitative methods of deep observation for understanding problematic online speech. This initial qualitative study constructs an ontology of problematic speech, which contains social media postings annotated with their underlying opinions. The qualitative study also dynamically constructs the set of opinions, simultaneous with labeling the postings. Next, we collect a large dataset from three online social media platforms (Facebook, Twitter and Youtube) using keywords. Finally, we introduce an iterative data exploration procedure to augment the dataset. It alternates between a data sampler, which balances exploration and exploitation of unlabeled data, the automatic labeling of the sampled data, the manual inspection by the qualitative mapping team and, finally, the retraining of the automatic opinion classifier. We present both qualitative and quantitative results. First, we present detailed case studies of the dynamics of problematic speech in a far-right Facebook group, exemplifying its mutation from conservative to extreme. Next, we show that our method successfully learns from the initial qualitatively labeled and narrowly focused dataset, and constructs a larger dataset. Using the latter, we examine the dynamics of opinion emergence and co-occurrence, and we hint at some of the pathways through which extreme opinions creep into the mainstream online discourse.
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