Pith. sign in

REVIEW 2 cited by

Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1708.09085 v1 pith:KL34XW63 submitted 2017-08-30 cs.CL

classification cs.CL
keywords argumentargumentsaudienceconvincedemotionalissuesmediaonline
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

Americans spend about a third of their time online, with many participating in online conversations on social and political issues. We hypothesize that social media arguments on such issues may be more engaging and persuasive than traditional media summaries, and that particular types of people may be more or less convinced by particular styles of argument, e.g. emotional arguments may resonate with some personalities while factual arguments resonate with others. We report a set of experiments testing at large scale how audience variables interact with argument style to affect the persuasiveness of an argument, an under-researched topic within natural language processing. We show that belief change is affected by personality factors, with conscientious, open and agreeable people being more convinced by emotional arguments.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Communication is All You Need: Persuasion Dataset Construction via Multi-LLM Communication

    cs.CL 2025-02 conditional novelty 6.0 of 10

    A six-role multi-LLM communication framework generates persuasive dialogue data that human judges find nearly indistinguishable from human-written rewrites.

  2. Was that Sarcasm?: A Literature Survey on Sarcasm Detection

    cs.CL 2024-11 conditional novelty 1.0 of 10

    A literature review that catalogs sarcasm detection datasets, word-embedding strategies, and neural models, but adds no new experimental results.

Pith tools