Don’t Let Me Be Misunderstood:Comparing Intentions and Perceptions in Online Discussions

Chang, Jonathan P.; Cheng, Justin; Danescu-Niculescu-Mizil, Cristian

doi:10.1145/3366423.3380273

Cited by 26 publications

(15 citation statements)

References 43 publications

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“…Recent studies have sought to analyze various aspects of antisocial users, such as their activity patterns [13,44], their evolution from the time they join a community until they become toxic [16], and the primary triggers of their behavior [15]. Another line of work has focused on detecting antisocial behavior by analyzing the relationship between the instigators and their targets, including their linguistic similarities [35,55], their shared social context [41], their personalities [20], and the misalignment between their intentions and perceptions [11]. Other studies have explored antisocial behavior at the community level, including inter-community conflict [33], the maintenance of toxic community norms [42], and the effects of major negative events [34].…”

Section: Further Related Workmentioning

confidence: 99%

The Structure of Toxic Conversations on Twitter

Saveski

Roy

2021

Proceedings of the Web Conference 2021

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Social media platforms promise to enable rich and vibrant conversations online; however, their potential is often hindered by antisocial behaviors. In this paper, we study the relationship between structure and toxicity in conversations on Twitter. We collect 1.18M conversations (58.5M tweets, 4.4M users) prompted by tweets that are posted by or mention major news outlets over one year and candidates who ran in the 2018 US midterm elections over four months. We analyze the conversations at the individual, dyad, and group level. At the individual level, we find that toxicity is spread across many low to moderately toxic users. At the dyad level, we observe that toxic replies are more likely to come from users who do not have any social connection nor share many common friends with the poster. At the group level, we find that toxic conversations tend to have larger, wider, and deeper reply trees, but sparser follow graphs. To test the predictive power of the conversational structure, we consider two prediction tasks. In the first prediction task, we demonstrate that the structural features can be used to predict whether the conversation will become toxic as early as the first ten replies. In the second prediction task, we show that the structural characteristics of the conversation are also predictive of whether the next reply posted by a specific user will be toxic or not. We observe that the structural and linguistic characteristics of the conversations are complementary in both prediction tasks. Our findings inform the design of healthier social media platforms and demonstrate that models based on the structural characteristics of conversations can be used to detect early signs of toxicity and potentially steer conversations in a less toxic direction.

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Section: Further Related Workmentioning

confidence: 99%

The Structure of Toxic Conversations on Twitter

Saveski

Roy

2021

Proceedings of the Web Conference 2021

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“…Other examples commonly observed in communication include, but are not limited to, formality (Rao and Tetreault, 2018) and emotional tones (Chhaya et al, 2018;Raji and de Melo, 2020). As we are provided with more opportunities to interact with people across cultural and language barriers, the risk of misunderstandings in communication also grows (Chang et al, 2020a). Thus, it is all the more important to develop tools to mitigate such risk and help foster mutual understandings.…”

Section: Discussionmentioning

confidence: 99%

Facilitating the Communication of Politeness through Fine-Grained Paraphrasing

Fussell

Danescu-Niculescu-Mizil

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Aided by technology, people are increasingly able to communicate across geographical, cultural, and language barriers. This ability also results in new challenges, as interlocutors need to adapt their communication approaches to increasingly diverse circumstances. In this work, we take the first steps towards automatically assisting people in adjusting their language to a specific communication circumstance. As a case study, we focus on facilitating the accurate transmission of pragmatic intentions and introduce a methodology for suggesting paraphrases that achieve the intended level of politeness under a given communication circumstance. We demonstrate the feasibility of this approach by evaluating our method in two realistic communication scenarios and show that it can reduce the potential for misalignment between the speaker's intentions and the listener's perceptions in both cases.

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“…Moral Foundation Dictionary is also incorporated to assist the prediction of moral values involved in Twitter posts [80] . Besides emotion and happiness, dictionary-driven representations are also extensively used to detect depression in social media [105,158] . Despite the wide adoption of dictionary-driven representations, Jaidka et al [77] made a comparison between unsupervised dictionary-driven and supervised data-driven methods, and verified that the latter is more robust for well-being estimation from social media data.…”

Section: A11 Symbol-based Representationmentioning

confidence: 99%

From Symbols to Embeddings: A Tale of Two Representations in Computational Social Science

et al. 2021

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Computational Social Science (CSS), aiming at utilizing computational methods to address social science problems, is a recent emerging and fast-developing field. The study of CSS is data-driven and significantly benefits from the availability of online user-generated contents and social networks, which contain rich text and network data for investigation. However, these large-scale and multi-modal data also present researchers with a great challenge: how to represent data effectively to mine the meanings we want in CSS?To explore the answer, we give a thorough review of data representations in CSS for both text and network.Specifically, we summarize existing representations into two schemes, namely symbol-based and embeddingbased representations, and introduce a series of typical methods for each scheme. Afterwards, we present the applications of the above representations based on the investigation of more than 400 research articles from 6 top venues involved with CSS. From the statistics of these applications, we unearth the strength of each kind of representations and discover the tendency that embedding-based representations are emerging and obtaining increasing attention over the last decade. Finally, we discuss several key challenges and open issues for future directions. This survey aims to provide a deeper understanding and more advisable applications of data representations for CSS researchers.

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Don’t Let Me Be Misunderstood:Comparing Intentions and Perceptions in Online Discussions

Cited by 26 publications

References 43 publications

The Structure of Toxic Conversations on Twitter

The Structure of Toxic Conversations on Twitter

Facilitating the Communication of Politeness through Fine-Grained Paraphrasing

From Symbols to Embeddings: A Tale of Two Representations in Computational Social Science

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