Mapping the Early Language Environment Using All-Day Recordings and Automated Analysis.

Jill Gilkerson, Jeffrey A Richards, Steven F Warren, Judith K Montgomery, Charles R Greenwood, D Kimbrough Oller, John H L Hansen, Terrance D Paul

May 2017 Am J Speech Lang Pathol

Synopsis of Social media discussions

The collection of discussions reflects a balanced view, with some participants noting replication efforts and possible biases, such as mentions of the 'word gap' being closer to 4 million words and concerns about racial bias, showing critical thinking about both the methodological and societal implications of the research. The tone varies from technical analysis to curiosity about the findings' real-world significance.

A
Agreement
 Neither agree nor disagree

The discussions show mixed opinions; some acknowledge the study's validity, while others point out issues like variability and replication concerns.

I
Interest
 Moderate level of interest

Participants are somewhat interested, with several referencing the potential implications and replication efforts, but overall curiosity remains moderate.

E
Engagement
 Moderate level of engagement

Comments include technical details, references to related studies, and debates about methodology, indicating some level of thoughtful engagement.

I
Impact
 Moderate level of impact

The discussions highlight the importance of the research, especially in the context of developmental and linguistic assessments, suggesting a modest perceived impact.

Social Mentions

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21 Posts

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6 Articles

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99 Articles

Metrics

Video Views

167

Total Likes

63

Extended Reach

199,378

Social Features

130

Timeline: Posts about article

Top Social Media Posts

Posts referencing the article

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  • エミリー
    @emily_luvcat (Twitter)

    ⤴️の2017年の研究はこれね。 https://t.co/zxZi0Inspu
    view full post

    October 31, 2025

    3

  • Sarah
    @4whossayingwhat (Twitter)

    @kapelianis @HeathMayo This was covered in one of my ECE classes. The original study has some issues but got the fundamentals right. https://t.co/XZjgtwFK2E https://t.co/pQMAlZInrr https://t.co/Mal1F4lHou
    view full post

    July 6, 2023

  • George
    @gorge_lilley (Twitter)

    @TERPodcast 3/ Another much larger study concluded the gap was about 4 million - https://t.co/vSCBo1h9mM
    view full post

    January 4, 2023

    1

  • Delip Rao e/σ
    @deliprao (Twitter)

    GPT-3 is trained on 200 billion words. An average 13 year old sees at most a 100 million words in their lifetime. So for every word this teenager sees, GPT3 has seen over 2000 words! https://t.co/RFYvPVUGCt
    view full post

    December 28, 2022

    40

  • Alex Warstadt
    @a_stadt (Twitter)

    @symbolicstorage It's really an order or magnitude estimate. Hart & Risley (1993) say 10M words per year is on the upper end. Gilkerson et al's (2017) much better data extrapolates to about 3-6M words per year. https://t.co/GZCeQG3z17
    view full post

    August 22, 2022

    1

  • Ian Cushing
    @ian_cushing (Twitter)

    @EALDominicB https://t.co/tXCvonNjaV
    view full post

    June 29, 2022

    4

  • Ian Cushing
    @ian_cushing (Twitter)

    @jnyrose https://t.co/tXCvonNjaV
    view full post

    June 29, 2022

    2

  • Matthias Prohl
    @ma_promat (Twitter)

    @R3gretting @titiatscriptor Das Experiment ist 2014-16 methodisch sauberer wiederholt worden. Ergebnisse waren im Kern gleich - wichtig war jedoch die hohe Varianz innerhalb der Gruppen. Es reicht nicht den Brockhaus vom Band abzuspielen, sondern es geht um die aktive Interaktion. https://t.co/gie0oUjYZ8 https://t.co/I29tjiTYVc
    view full post

    August 25, 2021

  • Jim Bloom
    @jimmyroybloom (Twitter)

    https://t.co/pg1sZqgJvx
    view full post

    October 7, 2020

  • tatiana_almeida
    @rouge_tatiana (Twitter)

    RT @LogPv: Mapping the Early Language Environment Using All-Day Recordings and Automated Analysis. https://t.co/Fk4AZq9TAn
    view full post

    July 23, 2020

    2

  • ColeLogopedasMadrid
    @ColeLogoMadrid (Twitter)

    RT @LogPv: Mapping the Early Language Environment Using All-Day Recordings and Automated Analysis. https://t.co/Fk4AZq9TAn
    view full post

    July 23, 2020

    2

  • Colegio Logopedas PV
    @LogPv (Twitter)

    Mapping the Early Language Environment Using All-Day Recordings and Automated Analysis. https://t.co/Fk4AZq9TAn
    view full post

    July 22, 2020

    3

    2

  • Matt Krause
    @prokraustinator (Twitter)

    @DavidPetrus94 (Data from Gilkerson et al., 2017): https://t.co/70cuJKF03J)
    view full post

    July 20, 2020

  • Linguistics Girl MLS MS
    @LinguisticsGirl (Twitter)

    A recent replication of the "30 million word gap" study found the "word gap" may be closer to 4 million words. That's quite a difference. Not to mention the racial bias in the original study, among other criticisms. https://t.co/Iix7YFQuFy https://t.co/sPtqISRg59 https://t.co/JiKY4Z1PBI
    view full post

    March 9, 2020

    1

  • Alphonse
    @corasundae (Twitter)

    @amyvaz3 It's not unique words, it is in the millions (4 million), and it has been replicated with a larger sample. https://t.co/0IREthhOKA
    view full post

    September 16, 2019

  • Alphonse
    @corasundae (Twitter)

    @Aldowyn @eveewing @JessicaCalarco https://t.co/0IREthhOKA It's been replicated though?
    view full post

    September 16, 2019

  • Alphonse
    @corasundae (Twitter)

    @jaybeware https://t.co/s8CbC6rHvE https://t.co/0IREthhOKA He's citing this study, not that one.
    view full post

    September 16, 2019

  • Sharon Vince
    @SharonMarie80 (Twitter)

    @etaknipsa https://t.co/bhumfnYXH6
    view full post

    June 25, 2019

    1

  • ASHA Journals
    @ASHAJournals (Twitter)

    (9 of 11) One of our MOST SHARED ARTICLES OF 2018! Mapping the Early #Language Environment Using All-Day Recordings & Automated Analysis https://t.co/ybOWInXB5L @LENAEarlyTalk @kulifespan @uofmemphis @UT_Dallas @DKimbroughOller #AJSLP #ASHAJournals @SIGPerspectives https://t.co/MR6WJA1kP2
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    March 3, 2019

    6

    1

  • CLaE
    @leafs_s (Twitter)

    RT @leafs_s: American Journal of Speech-Language Pathology 17 May 2017 Mapping the Early Language Environment Using All-Day Recordings and…
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    February 19, 2019

    1

  • CLaE
    @leafs_s (Twitter)

    American Journal of Speech-Language Pathology 17 May 2017 Mapping the Early Language Environment Using All-Day Recordings and Automated Analysis https://t.co/ReyfnamjBd
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Abstract Synopsis

  • This research used automated recordings and analysis to create a standardized way to measure early language environments in children from ages 2 to 48 months, comparing these measures to traditional language assessments and socioeconomic factors.
  • Children’s vocalizations and conversational exchanges increased with age, while adult word exposure remained steady after infancy; children from lower socioeconomic backgrounds had fewer vocalizations, interactions, and words, though there was high variability within groups.
  • The findings provide new insights into early language environments and have potential clinical uses for identifying children at risk of poor language development due to limited language exposure.