Evidence map›Paper›PMID 34151320›Full record

ArticleProceedings of the ... International World-Wide Web Conference. International WWW Conference2021

Contrastive Lexical Diffusion Coefficient: Quantifying the Stickiness of the Ordinary.

Mohammadzaman Zamani, H Andrew Schwartz

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of the ... International World-Wide Web Conference. International WWW Conference, 2021. 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

2 authors.

Mohammadzaman ZamaniStony Brook University, New York, USA.
H Andrew SchwartzStony Brook University, New York, USA.

Funding

Large-scale Data Scientific Assessment of Unhealthy Alcohol Consumption Among Front-Line Restaurant WorkersR01AA028032 · NIAAA · STATE UNIVERSITY NEW YORK STONY BROOK · PI SCHWARTZ, HANSEN ANDREW · 2020 to 2023
$2.5M
SCH: Advancing Language-based Analyses of Social Media to Reliably Monitor Variation in PopulationR01MH125702 · NIMH · STATE UNIVERSITY NEW YORK STONY BROOK · PI EICHSTAEDT, JOHANNES C., SCHWARTZ, HANSEN ANDREW · 2021 to 2024
$1.2M
NIAAA NIH HHS R01 AA028032NIMH NIH HHS R01 MH125702
6 · The paper itself

Abstract

Lexical phenomena, such as clusters of words, disseminate through social networks at different rates but most models of diffusion focus on the discrete adoption of new lexical phenomena (i.e. new topics or memes). It is possible much of lexical diffusion happens via the changing rates of existing word categories or concepts (those that are already being used, at least to some extent, regularly) rather than new ones. In this study we introduce a new metric,

Indexed as

Diffusion ModelLanguage ChangeLexical DiffusionOrdinary Language

Identifiers

PMID34151320
PMCPMC8211116

What OpenQuestion holds

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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.