Evidence map›Paper›PMID 42789193›Full record

ArticleJournal of psycholinguistic research2026

Emotional Dialogue Predicts the Success of Prestige Television Episodes.

Neil Franchuk, Jun Davidson-Lau, Raymond A Mar

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of psycholinguistic research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Neil FranchukDepartment of Psychology, York University, 4700 Keele St. W., Toronto, ON, M3J1P3, Canada.
Jun Davidson-LauDepartment of Psychology, York University, 4700 Keele St. W., Toronto, ON, M3J1P3, Canada.
Raymond A MarDepartment of Psychology, York University, 4700 Keele St. W., Toronto, ON, M3J1P3, Canada. mar@yorku.ca.ORCID http://orcid.org/0000-0002-5307-7031

Funding

Social Sciences and Humanities Research Council of Canada 435-2026-1867
6 · The paper itself

Abstract

Whether a narrative succeeds in satisfying its audience is of paramount importance, but predicting this success is difficult. Although previous research has found that the textual characteristics of novels can predict how they are received, what about multimodal narratives like film and television? In a television show, the only text available is the dialogue, with so much of the story conveyed non-verbally (e.g., facial expressions and actions, cinematography, editing, music). Although dialogue is just one aspect of a television episode, might the emotional nature of the dialogue still be able to explain some of an episode's appeal? We gathered all of the dialogue for 45 prestige television series that vary widely in terms of genre, setting, themes, and characters. The ability for the dialogue to explain the success of these episodes was then examined within a series, to hold constant many other factors (e.g., writers, producers, characters, themes, marketing), using bidirectional stepwise linear regression models. The emotional nature of the dialogue spoken within an episode managed to successfully predict audience ratings of these episodes within a series. Moreover, these models proved to be robust and able to predict data "out of sample." This result demonstrates that the emotional characteristics of dialogue alone can help explain the audience appeal of multimodal narratives, highlighting the importance of language even in the context of rich audiovisual information. Our findings have implications for those tasked with crafting compelling television stories.

Indexed as

EmotionsNarrationTelevisionHumansCultural successEmotionNarrative fictionNatural language processingTelevision series

Identifiers

PMID42789193

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.