# The optional realization of the French negative particle (ne) on Twitter: Space, status and time

Jacobo Levy Abitbol, Jean-Pierre Chevrot, Márton Karsai, Jean-Philippe Magué, Yannick Léo, Aurélie Nardy, Eric Fleury

## ► To cite this version:

Jacobo Levy Abitbol, Jean-Pierre Chevrot, Márton Karsai, Jean-Philippe Magué, Yannick Léo, et al. The optional realization of the French negative particle (ne) on Twitter: Space, status and time. New Ways of Analyzing Variation 46, Nov 2017, Madison, United States. hal-01832374

HAL Id: hal-01832374

https://hal.science/hal-01832374v1

Submitted on 9 Feb 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# The optional realization of the French negative particle (ne) on Twitter : Space, status and time

J. Levy Abitbol¹, J.-P. Chevrot², M. Karsai¹, J-P. Magué³, Y. Léo¹, A. Nardy² and E. Fleury¹

1. DANTE, IXXI, ENS Lyon 2. Lidilem, Université Grenoble Alpes 3. ICAR, ENS Lyon, France

Contact : jean-pierre.chevrot@univ-grenoble-alpes.fr

**From its beginnings, sociolinguistics strongly emphasized the need for data**

**Quantity** - Data allowing quantitative analysis of language variation and change

**Quality** - Data that are « good enough » for representing the actual language usage of the speech community

Rise of Computational Sociolinguistics: Study of language variation and change in large databases from digital interactions (blogs, social media, mobile phones, peer-to-peer services, etc.) (Nguyen et al., 2016)

## 1. General questions

- Can digital data reveal **new** sociolinguistic patterns?

- What is the risk of bias and how to overcome it?

- What is the benefit for sociolinguistic theory?

## 2. The Sosweet project

SOSWEET A sociolinguistics of Twitter http://sosweet.inria.fr/

**Interdisciplinarity** : Sociolinguistics + NLP + Data science + Corpus linguistics

**Four-year collection of 15% of the tweets**

- That are written in French

- By users located in the GTM and GTM+1 areas (French-speaking countries)

+ Recording of the network of follower/followee links

**Current state of the database**

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## 3. Sociolinguistic variable: Negative *ne*

Standard

NE + verb + Part. 2 : *pas* 'not', *jamais* 'never', *rien* 'nothing'...

Il ne boit jamais de vin
'He never drinks wine'

Non standard

Deletion of ne

Il ∅ boit jamais de vin

<table><thead><tr><th>Factors that influence NE realisation</th><th>References</th></tr></thead><tbody><tr><td>More NE realisation in older speakers</td><td>Ashby, 2001; Blattner & Williams, 2011; Hansen & Malderez, 2004</td></tr><tr><td>More NE realisation in the more formal settings and emphatic contexts</td><td>Armstrong, 2002; Williams, 2009; van Compernolle, 2009</td></tr><tr><td>More NE realisation if the subject of the verb is a NP (vs. a clitic) and with certain Part. 2</td><td>Ashby, 1981; Armstrong & Smith, 2002; Coveney, 1996; Hansen & Malderez, 2004, van Compernolle, 2008</td></tr><tr><td>Effect of Socioeconomic status : no convergence</td><td>Blattner & Williams, 2001; Williams, 2009; Hansen & Malderez</td></tr></tbody></table>

<table><thead><tr><th>Standard</th><th>Non Standard</th></tr></thead><tbody><tr><td><p><strong>Emmanuel Macron</strong> • <em>'I do not have the same fights as you but I'm as free as you'</em></p><p>Je n'ai pas les mêmes combats que vous mais je suis aussi libre que vous. #LEmissionPolitique</p></td><td><p><strong>Mister X</strong> • <em>'I seem to have lost track: is Cannes vs. Netflix the new Taxi vs. Uber?'</em></p><p><u>J'ai pas</u> suivi le truc, Cannes vs Netflix c'est le nouveau Taxi vs Uber ?</p></td></tr></tbody></table>

## 4. Inferring socioeconomic status (SES) from space...

**Using INSEE* data**

*French National Institute of Statistics and Economics Studies

Set of sociodemographic aggregated indicators for each

200m x 200m square areas across France

- Mean yearly income (without extreme values)

- Rate of homeowners

- Population density

**Working hypothesis**

Living in an area with higher income mean, with bigger rate of owners and with smaller density (single-family house) is probabilistically associated with higher SES

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**Matching INSEE data and 109 000 geolocated users of Twitter**

**INSEE Data**

Mean yearly income is 18 to 20 thousands of euros in this area

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**Working hypotheses**

- User #878291414 lives in this area

- The yearly income of user #878291414 is 18 to 20 thousands euros

## 5. Result: Space

Rate of standard negation per region

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**Trends**

Departments of South are more standard than departments of North. Difference is gradient

**Discussion**

- No clear dialectal explanation

- Are twitter users the same in the North and the South (sex, SES, network structure)?

## 6. Result: SES

Rate of standard ne realization

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Income

Rate of owners

Population density

**Trends**

Standard increases when

- Income/rate of owners increase

- Population density decreases

**Discussion**

Correlations

- Surprisingly strong

- With small range of sociolinguistic variation

## 7. Result: Time

Rate of standard ne realization

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**Trends**

- Daily cycle of standard: max. in the morning, min. in the night ?

- People active during the day have higher income (warmer colors)

**Discussion**

Effect of the changing composition of Twitter users

## 8. Conclusion

**The speaker is exposed to “big data”**

- A child perceives 700 utterances each “non-slept” hour (Cameron-Faulkner et al., 2003). One can estimate that a 30-year old speaker has perceived 100M. utterances (700 x 13 x 365 x 30)

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Does big data tell us something about the cumulated experience of the speaker?

**Next steps**

- Control confounds, test others sociolinguistic variables, compute predictions