Recognizing Counterfactual Thinking in Social Media Texts

Son, Youngseo; Buffone, Anneke; Raso, Joe; Larche, Allegra; Janocko, Anthony; Zembroski, Kevin; Schwartz, H. Andrew; Ungar, Lyle H.

doi:10.18653/v1/p17-2103

Cited by 31 publications

(41 citation statements)

References 15 publications

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“…To supplement our analyses, we compute several coarse-grained lexical counts for each story in HIPPOCORPUS. Such approaches have been used in prior efforts to investigate author mental states, temporal orientation, or counterfactual thinking in language (Tausczik and Pennebaker, 2010;Schwartz et al, 2015;Son et al, 2017).…”

Section: Lexical and Stylistic Measuresmentioning

confidence: 99%

Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models

Sap

Horvitz

Choi

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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We investigate the use of NLP as a measure of the cognitive processes involved in storytelling, contrasting imagination and recollection of events. To facilitate this, we collect and release HIPPOCORPUS, a dataset of 7,000 stories about imagined and recalled events.We introduce a measure of narrative flow and use this to examine the narratives for imagined and recalled events. Additionally, we measure the differential recruitment of knowledge attributed to semantic memory versus episodic memory (Tulving, 1972) for imagined and recalled storytelling by comparing the frequency of descriptions of general commonsense events with more specific realis events.Our analyses show that imagined stories have a substantially more linear narrative flow, compared to recalled stories in which adjacent sentences are more disconnected. In addition, while recalled stories rely more on autobiographical events based on episodic memory, imagined stories express more commonsense knowledge based on semantic memory. Finally, our measures reveal the effect of narrativization of memories in stories (e.g., stories about frequently recalled memories flow more linearly; Bartlett, 1932). Our findings highlight the potential of using NLP tools to study the traces of human cognition in language. * Research conducted during an internship at Microsoft Research.

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Section: Lexical and Stylistic Measuresmentioning

confidence: 99%

Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models

Sap

Horvitz

Choi

et al. 2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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“…Causal Semantic Detection: Recently, causality detection which detects specific causes and effects and the relations between them has received more attention, such as the researches proposed by Li (Li and Mao, 2019), Zhang (Zhang et al, 2017), Bekoulis (Bekoulis et al, 2018), Do (Do et al, 2011, Riaz (Riaz and Girju, 2014), Dunietz (Dunietz et al, 2017a) and Sharp (Sharp et al, 2016). Specifically, to extract the causal explanation semantics from the messages in a general level, some researches capture the causal semantics in messages from the perspective of discourse structure, such as capturing counterfactual conditionals from a social message with the PDTB discourse relation parsing (Son et al, 2017), a pre-trained model with Rhetorical Structure Theory Discourse Treebank (RSTDT) for exploiting discourse structures on movie reviews (Ji and Smith, 2017), and a two-step interactive hierarchical Bi-LSTM framework (Xia and Ding, 2019) to extract emotion-cause pair in messages. Furthermore, Son (2018) defines the causal explanation analysis task (CEA) to extract causal explanatory semantics in messages and annotates a dataset for other downstream tasks.…”

Section: Related Workmentioning

confidence: 99%

Towards Causal Explanation Detection with Pyramid Salient-Aware Network

Zuo

Chen

Liu

et al. 2020

Lecture Notes in Computer Science

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Causal explanation analysis (CEA) can assist us to understand the reasons behind daily events, which has been found very helpful for understanding the coherence of messages. In this paper, we focus on Causal Explanation Detection, an important subtask of causal explanation analysis, which determines whether a causal explanation exists in one message. We design a Pyramid Salient-Aware Network (PSAN) to detect causal explanations on messages. PSAN can assist in causal explanation detection via capturing the salient semantics of discourses contained in their keywords with a bottom graph-based word-level salient network. Furthermore, PSAN can modify the dominance of discourses via a top attention-based discourse-level salient network to enhance explanatory semantics of messages. The experiments on the commonly used dataset of CEA shows that the PSAN outperforms the state-of-the-art method by 1.8% F1 value on the Causal Explanation Detection task.

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“…Argumentation mining in social media has given rise to new tasks such as detecting agreements and disagreement in conversations (Allen, Carenini, and Ng 2014), counterfactual recognition (Son et al 2017), identification of controversial topics (Addawood and Bashir 2016), stance/rumor detection (Zubiaga et al 2016), and fact-checking (Baly et al 2018). Argumentation and stancetaking are further discussed later in this special issue (cf.…”

Section: Argumentationmentioning

confidence: 99%

Introduction to the Special Issue on Language in Social Media: Exploiting Discourse and Other Contextual Information

Benamara

Inkpen

Taboada

2018

Computational Linguistics

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Social media content is changing the way people interact with each other and share information, personal messages, and opinions about situations, objects, and past experiences. Most social media texts are short online conversational posts or comments that do not contain enough information for natural language processing (NLP) tools, as they are often accompanied by non-linguistic contextual information, including meta-data (e.g., the user’s profile, the social network of the user, and their interactions with other users). Exploiting such different types of context and their interactions makes the automatic processing of social media texts a challenging research task. Indeed, simply applying traditional text mining tools is clearly sub-optimal, as, typically, these tools take into account neither the interactive dimension nor the particular nature of this data, which shares properties with both spoken and written language. This special issue contributes to a deeper understanding of the role of these interactions to process social media data from a new perspective in discourse interpretation. This introduction first provides the necessary background to understand what context is from both the linguistic and computational linguistic perspectives, then presents the most recent context-based approaches to NLP for social media. We conclude with an overview of the papers accepted in this special issue, highlighting what we believe are the future directions in processing social media texts.

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Recognizing Counterfactual Thinking in Social Media Texts

Cited by 31 publications

References 15 publications

Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models

Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models

Towards Causal Explanation Detection with Pyramid Salient-Aware Network

Introduction to the Special Issue on Language in Social Media: Exploiting Discourse and Other Contextual Information

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