Weakly-Supervised Hierarchical Models for Predicting Persuasive Strategies in Good-faith Textual Requests

Chen, Jiaao; Yang, Diyi

doi:10.1609/aaai.v35i14.17498

Cited by 7 publications

(10 citation statements)

References 19 publications

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“…Among the limited prior works, tried to answer the question of which image is more persuasive; and Joo et al (2014) introduced syntactical and intent features such as facial displays, gestures, emotion, and personality, which result in persuasive images. On the other hand, decoding persuasion in textual content has been extensively studied in natural language processing from both extractive and generative contexts (Habernal and Gurevych, 2016;Chen and Yang, 2021;Luu et al, 2019). All of the marketing messages employ one of a set of strategies to persuade their target customers.…”

Section: Related Workmentioning

confidence: 99%

A Video Is Worth 4096 Tokens: Verbalize Story Videos To Understand Them In Zero Shot

Bhattacharyya,

Singla,

Krishnamurthy

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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Multimedia content, such as advertisements and story videos, exhibit a rich blend of creativity and multiple modalities. They incorporate elements like text, visuals, audio, and storytelling techniques, employing devices like emotions, symbolism, and slogans to convey meaning. There is a dearth of large annotated training datasets in the multimedia domain hindering the development of supervised learning models with satisfactory performance for real-world applications. On the other hand, the rise of large language models (LLMs) has witnessed remarkable zero-shot performance in various natural language processing (NLP) tasks, such as emotion classification, questionanswering, and topic classification. To leverage such advanced techniques to bridge this performance gap in multimedia understanding, we propose verbalizing long videos to generate their descriptions in natural language, followed by performing video-understanding tasks on the generated story as opposed to the original video. Through extensive experiments on fifteen video-understanding tasks, we demonstrate that our method, despite being zero-shot, achieves significantly better results than supervised baselines for video understanding. Furthermore, to alleviate a lack of story understanding benchmarks, we publicly release the first dataset on a crucial task in computational social science on persuasion strategy identification.

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Section: Related Workmentioning

confidence: 99%

A Video Is Worth 4096 Tokens: Verbalize Story Videos To Understand Them In Zero Shot

Bhattacharyya,

Singla,

Krishnamurthy

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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“…Various classification schemes have been applied to rhetorical strategies. Several papers have proposed to use schemes guided form social psychology on persuasion (Young et al, 2011;Chen and Yang, 2021). Chen and Yang (2021) argue that their taxonomy can be used to unify the modelling of persuasion strategies.…”

Section: Classification Schemesmentioning

confidence: 99%

“…Several papers have proposed to use schemes guided form social psychology on persuasion (Young et al, 2011;Chen and Yang, 2021). Chen and Yang (2021) argue that their taxonomy can be used to unify the modelling of persuasion strategies. Their scheme uses the following labels: Commitment, Emotion, Politeness, Reciprocity, Scarcity, Credibility, Evidence and Impact (Chen and Yang, 2021).…”

Section: Classification Schemesmentioning

confidence: 99%

“…Chen and Yang (2021) argue that their taxonomy can be used to unify the modelling of persuasion strategies. Their scheme uses the following labels: Commitment, Emotion, Politeness, Reciprocity, Scarcity, Credibility, Evidence and Impact (Chen and Yang, 2021). The strategy labels "credibility" and "emotion" are linked to respectively ethos and pathos.…”

Section: Classification Schemesmentioning

confidence: 99%

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Modelling Persuasion through Misuse of Rhetorical Appeals

Паули¹,

Derczynski²,

Assent³

2022

Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI)

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It is important to understand how people use words to persuade each other. This helps understand debate, and detect persuasive narratives in regard to e.g. misinformation. While computational modelling of some aspects of persuasion has received some attention, a way to unify and describe the overall phenomenon of when persuasion becomes undesired and problematic, is missing. In this paper, we attempt to address this by proposing a taxonomy of computational persuasion. Drawing upon existing research and resources, this paper shows how to re-frame and re-organise current work into a coherent framework targeting the misuse of rhetorical appeals. As a study to validate these re-framings, we then train and evaluate models of persuasion adapted to our taxonomy. Our results show an application of our taxonomy, and we are able to detecting misuse of rhetorical appeals, finding that these are more often used in misinformative contexts than in true ones. Fallacy of PathosAppeal to Fear: "'Without this additional insurance, you could find yourself broke and homeless" Appeal to Pity: "I know I missed assignments, but if you fail me, I will lose my financial aid and have to drop out." Appeal to Popularity: "Nine out of ten shoppers have switched to Blindingly-Bright-Smile Toothpaste." Fallacy of EthosFalse authority:"Dr. X is an engineer, and he doesn't believe in global warming." Ad Hominem:"Why should we think a candidate who recently divorced will keep her campaign promises?" Name-calling:"These rabble-rousers are nothing but feminazis." Fallacy of LogosFalse dilemma:"Either we pass this ordinance or there will be rioting in the streets" Circular argument:"This legislation is sinful because it is the wrong thing to do." Red Herring or Smoke Screen:"My opponent says I am weak on crime, but I have been one of the most reliable participants in city council meetings.

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“…2020; Ziems et al, 2022). In addition to the time and cost inefficiency of such approaches, the construction and use of these banks of norms treat each sentence or segment in isolation, and they fail to take the dependencies between norms in the flow of a dialogue into account (Fung et al, 2022;Chen and Yang, 2021). For instance, it is most likely that a dialogue segment containing the norm of Request follows a segment that includes Request and Criticism (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

NormMark: A Weakly Supervised Markov Model for Socio-cultural Norm Discovery

Moghimifar¹,

Qu²,

Wu³

et al. 2023

Findings of the Association for Computational Linguistics: ACL 2023

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Norms, which are culturally accepted guidelines for behaviours, can be integrated into conversational models to generate utterances that are appropriate for the socio-cultural context. Existing methods for norm recognition tend to focus only on surface-level features of dialogues and do not take into account the interactions within a conversation. To address this issue, we propose NORMMARK, a probabilistic generative Markov model to carry the latent features throughout a dialogue. These features are captured by discrete and continuous latent variables conditioned on the conversation history, and improve the model's ability in norm recognition. The model is trainable on weakly annotated data using the variational technique. On a dataset with limited norm annotations, we show that our approach achieves higher F1 score, outperforming current stateof-the-art methods, including GPT3.

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Weakly-Supervised Hierarchical Models for Predicting Persuasive Strategies in Good-faith Textual Requests

Cited by 7 publications

References 19 publications

A Video Is Worth 4096 Tokens: Verbalize Story Videos To Understand Them In Zero Shot

A Video Is Worth 4096 Tokens: Verbalize Story Videos To Understand Them In Zero Shot

Modelling Persuasion through Misuse of Rhetorical Appeals

NormMark: A Weakly Supervised Markov Model for Socio-cultural Norm Discovery

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