How Much Does Persuasion Strategy Matter? LLM-Annotated Evidence from Charitable Donation Dialogues
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| Format: | Preprint |
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2026
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| _version_ | 1866914497503952896 |
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| author | Petrova, Tatiana Sokol, Stanislav State, Radu |
| author_facet | Petrova, Tatiana Sokol, Stanislav State, Radu |
| contents | Which persuasion strategies, if any, are associated with donation compliance? Answering this requires fine-grained strategy labels across a full corpus and statistical tests corrected for multiple comparisons. We annotate all 10,600 persuader turns in the 1,017-dialogue PersuasionForGood corpus (Wang et al., 2019), where donation outcomes are directly observable, with a taxonomy of 41 strategies in 11 categories, using three open-source large language models (LLMs; Qwen3:30b, Mistral-Small-3.2, Phi-4). Strategy categories alone explain little variance in donation outcome (pseudo $R^2 \approx 0.015$, consistent across all three annotators). Guilt Induction is the only strategy significantly associated with lower donation rates ($Δ\approx -23$ percentage points), an effect that replicates across all three models despite only moderate inter-model agreement. Reciprocity is the most robust positive correlate. Target sentiment and interest predict whether a donation occurs but show at most a weak correlation with donation amount. These findings suggest that strategy identification alone is insufficient to explain persuasion effectiveness, and that guilt-based appeals may be counterproductive in prosocial settings. We release the fully annotated corpus as a public resource. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_19783 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | How Much Does Persuasion Strategy Matter? LLM-Annotated Evidence from Charitable Donation Dialogues Petrova, Tatiana Sokol, Stanislav State, Radu Computation and Language I.2.7; J.4 Which persuasion strategies, if any, are associated with donation compliance? Answering this requires fine-grained strategy labels across a full corpus and statistical tests corrected for multiple comparisons. We annotate all 10,600 persuader turns in the 1,017-dialogue PersuasionForGood corpus (Wang et al., 2019), where donation outcomes are directly observable, with a taxonomy of 41 strategies in 11 categories, using three open-source large language models (LLMs; Qwen3:30b, Mistral-Small-3.2, Phi-4). Strategy categories alone explain little variance in donation outcome (pseudo $R^2 \approx 0.015$, consistent across all three annotators). Guilt Induction is the only strategy significantly associated with lower donation rates ($Δ\approx -23$ percentage points), an effect that replicates across all three models despite only moderate inter-model agreement. Reciprocity is the most robust positive correlate. Target sentiment and interest predict whether a donation occurs but show at most a weak correlation with donation amount. These findings suggest that strategy identification alone is insufficient to explain persuasion effectiveness, and that guilt-based appeals may be counterproductive in prosocial settings. We release the fully annotated corpus as a public resource. |
| title | How Much Does Persuasion Strategy Matter? LLM-Annotated Evidence from Charitable Donation Dialogues |
| topic | Computation and Language I.2.7; J.4 |
| url | https://arxiv.org/abs/2604.19783 |