Evidence map›Paper›PMID 41721073›Full record

ArticleBehavior research methods2026

ConversationAlign: Open-source software for analyzing patterns of lexical use and alignment in conversation transcripts.

Benjamin Sacks, Virginia Ulichney, Anna Duncan, Chelsea Helion, Sarah M Weinstein, Tania Giovannetti, Gus Cooney, Jamie Reilly

Abstract read
In one paragraph

Article in Behavior research methods, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Abstract word dropout and cross-speaker misalignment of word concreteness are features of conversation in aging.Cortex; a journal devoted to the study of the nervous system and behavior · 2025
    Article
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

8 authors.

Benjamin SacksDepartment of Psychology and Neuroscience, Temple University, 1701 N. 13th Street, Philadelphia, PA, 19122, USA.ORCID http://orcid.org/0009-0005-7323-6625
Virginia UlichneyDepartment of Psychology and Neuroscience, Temple University, 1701 N. 13th Street, Philadelphia, PA, 19122, USA.
Anna DuncanDepartment of Psychology and Neuroscience, Temple University, 1701 N. 13th Street, Philadelphia, PA, 19122, USA.
Chelsea HelionDepartment of Psychology and Neuroscience, Temple University, 1701 N. 13th Street, Philadelphia, PA, 19122, USA.
Sarah M WeinsteinDepartment of Epidemiology and Biostatistics, Temple University, Philadelphia, PA, USA.
Tania GiovannettiDepartment of Psychology and Neuroscience, Temple University, 1701 N. 13th Street, Philadelphia, PA, 19122, USA.
Gus CooneyDepartment of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
Jamie ReillyDepartment of Psychology and Neuroscience, Temple University, 1701 N. 13th Street, Philadelphia, PA, 19122, USA. reillyj@temple.edu.

Funding

Semantic Memory and Language Learning in Alzheimer's Disease and Semantic DementiR01DC013063 · NIDCD · TEMPLE UNIV OF THE COMMONWEALTH · PI James Joseph Reilly · 2014 to 2026
$6.8M
NIDCD NIH HHS R01 DC013063
6 · The paper itself

Abstract

Much of our scientific understanding of language processing has been informed by controlled experiments divorced from the real-world demands of naturalistic communication. Conversation requires synchronization of rate, amplitude, lexical complexity, affective coloring, shared reference, and countless other verbal and nonverbal dimensions. Conversation is not merely a vector for information transfer but also serves as a mechanism for establishing or maintaining social relationships. This process of language calibration between interlocutors is known as linguistic alignment. We developed an open-source R package, ConversationAlign, capable of computing novel indices of linguistic alignment and main effects of language use between interlocutors by evaluating word choice across numerous semantic, affective, and lexical dimensions (e.g., valence, concreteness, frequency, word length). We describe the operations of ConversationAlign, including its primary functions of cleaning and transforming raw language data into simultaneous time series objects aggregated by interlocutor, turn, and conversation. We then outline mathematical operations involved in computing complementary indices of linguistic alignment that capture both local (synchrony in turn-by-turn scores) and global relations (overall proximity) between interlocutors. We present a use case of ConversationAlign applied to interview transcripts from American radio legend Terry Gross and her many guests spanning 15 years. We identify caveats for use and potential sources of bias (e.g., polysemy, missing data, robustness to brief language samples) and close with a discussion of potential applications to other populations. ConversationAlign (v 0.4.0) is freely available for download and use via CRAN or GitHub. For technical instructions and download, visit https://github.com/Reilly-ConceptsCognitionLab/ConversationAlign .

Indexed as

CommunicationLanguageLinguisticsSoftwareHumansSemanticsAlignmentCommunicationConversation analysisLanguage

Identifiers

PMID41721073
PMCPMC12923439

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.