The Digital-Born and Legacy News Media on Twitter during the French Presidential Elections

Silvia Majó-Vázquez, Jun Zhao and Rasmus Kleis Nielsen

Key findings

In this RISJ Factsheet, we analyse a sample of 2.96 million news-related tweets from a larger dataset of 43.5 million tweets collected during the 2017 French Presidential elections to examine the role of digital-born and legacy news media in online political discussions in France.

We find that:

Legacy media, most notably newspapers and broadcasters, figure very prominently in the political discussion on Twitter. Legacy media generated more than seven times as much activity and engagement as digital-born news media during the election. 88.43% of news-related tweets collected either originated with or included explicit reference to legacy media, compared to 11.56% that originated with or included explicit reference to digital-born news media.

A high number of followers and frequent tweeting does not automatically translate into high levels of engagement from audiences. Some prominent newspapers (Le Monde and Liberation), broadcasters (TF1 and BFM TV), and digital-born news media (Mediapart and Brut) see high levels of engagement, but some newspapers and digital-born news media have much lower levels of engagement than their general audience reach, follower count, and their frequent tweeting would lead one to expect.

Attention and engagement is very unevenly distributed. A few media organisations like Liberation, TF1, and Mediapart are mentioned by others on average hundreds or even more than a thousand times for every time they tweet. In contrast, many news organisations, especially local and regional newspapers and smaller digital-born news media, find the level of engagement they receive does not correspond to the number of tweets they send, suggesting limited engagement. Interestingly, many of these organisations have a high ratio of following to followers – but despite thus engaging with many other accounts on Twitter see limited engagement in return.

Our findings suggest that news media organisations – especially a few prominent newspapers, broadcasters, and digital-born news media – figure very prominently in political discussions on Twitter, but also that a number of organisations with considerable audience reach, Twitter follower counts, and who tweet very frequently see much more limited engagement, and that local and regional newspapers as well as smaller digital-born news media see little engagement on Twitter.

General Overview

Digital-born and legacy media are competing to control the most central positions in the flow of online news. How this competition, which is even greater during major political events, unfolds on Twitter is still largely unknown. The 2017 French Presidential election offers a useful lens to investigate the role of digital-born and legacy media in one of the most important countries in Europe, and one that is different from the English-language markets most frequently analysed, both in terms of its political system and its media system.

The purpose of this RISJ Factsheet is to present an overview of 1) the allocation of news audience attention on Twitter during the French elections and 2) the potential influence of digital-born and legacy media in this platform of news distribution.

The French context is different from that found in many other markets in important ways. First, French digital-born news media are more prominent than in other European countries. Also legacy media, especially newspapers, have a historically weaker role here than in, for example, Germany or the UK (Nicholls, Shabbir, & Nielsen, 2016). Print newspapers have lower reach than in much of Northern Europe and although television remains the most important and widely used source of news in France, the overall audience is aging and eroding (Newman, Fletcher, Levy, & Nielsen, 2016). In contrast, the French increasingly turn to social media for news. 40% say they get news via social media, especially Facebook. 8% use Twitter for news, and the figures for Twitter, and for social media more widely, is higher for younger media users.

While still only used by a small minority as a source of news, Twitter has a broader political role that makes it important to understand political discussion there. In addition to serving as a platform for discussion and news distribution, political campaigns seek to use it to mobilise support and shape political discussions, and journalists and news media in turn pay attention to Twitter as they cover politics (Chadwick, 2013; Conway, Kenski, & Wang, 2015; Murthy, 2015; Quinlan et al., 2017; Stromer-Galley, 2014). We therefore want to analyse political discussion on Twitter during the French Presidential elections and specifically focus on the role of legacy and digital-born news media organisations in political discussions on Twitter.

Data Collection

Our dataset contains 2.96 million tweets collected between 2 April and 8 May 2017. We use a supervised third-party software for Twitter data collection, named Kalium, which calls the Streaming API. As Morstatter, Pfeffer, Liu, & Carley (2013) note the essential drawback of the Twitter Streaming API is the lack of information concerning what and how much data one gets. Yet we do know that it allows us to retrieve at most a 1% sample of all the data matching a set of pre-determined parameters (see Morstatter et al., 2013 for a systematic comparison between the Streaming API and the Twitter Firehose data).

Our focus here is on news-related tweets, which we gathered 1) by collecting all tweets from a pre-defined list of Twitter handles and 2) by collecting additional tweets on the basis of relevant hashtags identified seperately.

Identifying media outlets and individual accounts

We started by defining a sample of French news media outlets based on their overall audience reach. Using comScore online metrics, we averaged the individual audience reach for French media outlets during the last three months starting from April 2017. We obtained the ranking of the most visited news sites in France with at least a percentage reach of 0.03%. To avoid leaving out important news sites, not in terms of their reach but instead of their brand awareness, we double-checked our list of news media with a group of five French experts including researchers and journalists (see the Acknowledgements). They helped us to identify two main types of additional media outlets: recently founded news sites that are not indexed in comScore yet (e.g. Brut, cFactuel, Brief.me or Explicite); or popular news sites that fall at the extremes of the ideological spectrum (e.g. Égalité & Récontiliation, Fdesouche or Dreuz Info). In total, we studied 27 digital-born outlets and 71 legacy media (see the Appendix for the full list). Finally, we incorporated the list of key actors by including the name of the eleven French presidential candidates and their political parties.

We gathered all tweets sent by our pre-selected set of media and political actors. We also collected all tweets including their name or their Twitter username. Tweets where their names were mentioned in URLs embedded in text were also gathered.

Identifying tags

Previous research has already pointed out the drawbacks of collecting tweets based on usernames as well as the complexity of tracking a multithreaded social event such as an election campaign (Jungherr, 2016).

To minimise the risk of missing other relevant tweets during the elections, we completed this primary data collection strategy with a second approach. We also included news-related tweets from other users by identifying relevant hashtags in the overall French Twitter conversation by tracking trending conversations twice per day. We used several online tools for this purpose. The most important hashtags in terms of the overall conversation were included in our Twitter crawler too.

Overall, we gathered 43.5 million tweets through this combination of approaches, which we then filtered following the criteria specified in Figure 3 (at the end of the Factsheet). Finally, we obtained a sample of 2.96 million tweets. 718,000 of them are original tweets and 2.24m are retweets.1

News Content Activity

The first step in our analysis is to assess the distribution of news content volume throughout the electoral campaign cycle. Figure 1 shows the overall volume of tweets about the French Presidential elections by type of news media. The category legacy media includes national daily newspapers, monthly and weekly newspapers, regional and local daily newspapers, magazines, news agencies, radio, broadcasters and free national newspapers. Digital-born news media include a variety of news-related organisations launched online, most of them recently. The main findings of this longitudinal analysis are:

Legacy media figure far more prominently in news-related Tweets during the French Presidential elections than digital-born news media do. 88.43% of the news-related content on Twitter either originate with or include explicit reference to legacy media. Only 11.56% of this content originate with or include explicit reference to digital-born news media.

The volume of news-related Twitter activity is clearly driven by events and especially by the electoral cycle. Spikes in volume correspond to the most important stages of the electoral campaign i.e. electoral debates, the start of the campaign and the first and second polling days, as well as to events like the attack on the Champs Élysées.

The first electoral debate, or “Le Grand Débat”, that took place on 4 April and featured eleven presidential candidates saw the highest amount of news-related activity registered in one day on Twitter. More than 126,000 messages around that date included legacy media content or mentioned this type of media outlet.

Digital-born media content reached the highest volume on Twitter before the second and final polling day, but their output varies far less than that of legacy media and they account for much less activity overall.

Of note, the jump in the overall number of tweets related to legacy media content reached its highest point only hours after both polling days. This contrasted with the subsequent 24 hours after the first round of the elections, where there was a substantial drop in legacy media news content. As tweets were only collected until 8 April, (the day after the second polling day) it is not clear whether this drop took place again on 9 April. However, we do observe an even greater jump in the immediate hours after the second round.

Finally, the trend representing content by, or mentions to, digital-born outlets follows a more stable pattern with less spikes, whereas the line of legacy media shows deep falls and jumps before and after a salient electoral event.

Figure 1. Timeline of overall number of tweets by media type

Figure 1

Most Active Media

A more in-depth analysis looks at the individual activity on Twitter of the 98 media outlets under study. Table 1 includes the ranking of the top 20 most active news media as measured by the number of original tweets posted during the French campaign. At this level, we find that:

Le Figaro was the most active news media outlet overall.

Le Huffington Post is the only digital-born outlet amongst the top 20 most active news media.

Legacy media led the posting activity on Twitter by a great distance. National daily newspapers, broadcasters and radio are the most active categories.

Amongst the three least active media users in our sample we find Le Canard Enchainé. This is the weekly newspaper that first reported on the alleged financial scandals affecting François Fillon’s wife, known for its political satire (and its off-hand approach to digital media).

In this category of least active we also find two local daily newspapers, L’informateur d’Eu et L’Éclaireur and Le Républicain 47.

Table 1. Ranking of the 20 top most active media outlets on Twitter

Type

Brand

TW username

Tweets

National Daily Newpaper

Le Figaro

Le_Figaro

3198

TV

BFM TV

BFMTV

3004

TV

TF1/LCI

LCI

2183

National Daily Newpaper

Les Echos

LesEchos

2025

Radio

RMC

RMCinfo

1889

Radio

Europe 1

Europe1

1883

National Daily Newpaper

Le Monde

lemondefr

1675

Weekly Newspaper

L’Obs

lobs

1589

Weekly Newspaper

Le JDD

leJDD

1518

Free National Newspaper

20 Minutes

20Minutes

1366

TV

LCP

LCP

1242

Regional Newspaper

Ouest France

OuestFrance

1222

Magazine

Paris Match

ParisMatch

1195

Radio

RTL

RTLFrance

1155

News Agency

Agence France-Presse

afpfr

986

National Daily Newpaper

Liberation

libe

914

Weekly Newspaper

L’Express

LEXPRESS

876

TV

Franceinfo

franceinfo

871

Digital-born

Le Huffington Posts

LeHuffPost

866

Weekly Newspaper

Le Point

LePoint

863

Media Influence

So far, we have documented the overall importance of news media content during the French Presidential elections. The next step is to examine whether the prominence of legacy media and their high levels of Twitter activity are translated into greater levels of influence within the overall flow of news. To address this question we analyse the relation between the amount of news content produced and the number of mentions and replies received by each media outlet. Drawing on previous research on network role identification on Twitter (Gonzalez-Bailon, Borge-Holthoefer, & Moreno, 2013), we estimate the overall potential influence of each news outlet by relating their level of audience engagement to the relationship they establish with their followers. The basic idea here is that influence can be mapped along two dimensions. First, the ratio between how often an account tweets (messages sent) and how often it is mentioned or receives replies by others (messages received) and, second, the ratio between how many accounts it follows, and how many followers it has. In line with this, we first look at the ranking of most retweeted tweets2 (Table 2) and find that:

At the top of the ranking there is a digital-born outlet. A satirical video from Brut was posted at the end of the electoral campaign and one day before the final polling day. It obtained almost 40,000 retweets and over 25,000 likes.

At the aggregate level, tweets from prominent television broadcasters dominated the ranking of most retweeted messages.

All top ten most retweeted tweets contained some kind of audiovisual content, which speaks to the importance of compelling videos and pictures to boost the popularity of a news message on Twitter.

Finally, we look at the allocation of news audience attention by media brands and their relationship with their public (Figure 2) along the two dimensions of messages sent/received and following/followers, and find that:

The level of Twitter activity does not seem to always explain the overall influence of news media outlets as measured by number of mentions and replies received by news content.

The weekly newspaper Le Canard Enchainé reached the highest level of attention on Twitter during the French Presidential elections, although it was amongst the least active news media in this platform. This result highlights that offline news content also drives Twitter audience attention.

Similarly, several digital-borns exhibit a higher level of audience attention in comparison to their posting activity. This is the case of Brut (number of posts=84), Mediapart (n=435) and Explicité (n=55) amongst others.

The most active accounts, even when these are associated with very prominent news media, do not necessarily see particularly high levels of engagement on Twitter. The broadcasters BFM TV and TF1 have high general audience reach across offline and online media, high follower counts, and are very active on Twitter, and see high levels of engagement as measured by messages sent/received, but this is less so in the case of, for example, the newspapers Le Figaro and Les Echos. In contrast, news media like the newspaper Liberation and the digital-born news media site Mediapart seem far more influential on Twitter judging by overall engagement than their general online audience reach or their Twitter following alone would suggest.

The ratio of messages received and sent is negatively related to the ratio of following over followers. In other words, news media outlets that achieve higher levels of attention do not necessarily follow large numbers of other accounts. Particularly interesting here is how attention and engagement is very unevenly distributed, and how our data documents that many news organisations, especially local and regional newspapers and smaller digital-born news media, tweet almost as often as other Twitter users engage with their tweets, suggesting limited engagement. Interestingly, many of these organisations have a high ratio of following to followers – but despite thus engaging with many other accounts on Twitter see limited engagement in return.

Table 2. Most retweeted Tweets

Type

Brand

TW username

Text

RT Count

Favorite Count

Time

Digital-born

Brut

brutofficiel

Il était temps que ça se termine

39987

25322

06/05/2017 06:03

TV

France 24

france24_en

#BREAKING – Emmanuel #Macron elected president of France (with 65.1% of the vote)

22014

33985

07/05/2017 17:59

TV

TF1/TMC

qofficiel

« Je vous considère comme un bougnoule. » @AzzAhmedChaouch discute au défilé de Jean-Marie Le Pen : https://www.tf1.fr/tmc/quotidien-avec-yann-barthes/videos/front-national-fascistes-autres-anti-bougnoules.html … #Quotidien

13375

5192

01/05/2017 18:47

Digital-born

Brut

brutofficiel

Visionnaires.

13049

8357

07/05/2017 18:15

TV

TF1/TMC

qofficiel

Devant le QG de François Fillon, @HugoClement a fait une rencontre. Victoire, on vous embrasse. #Quotidien

11227

9555

24/04/2017 18:03

TV

TF1/TMC

qofficiel

« Tu vas me mettre en forme avant le discours, toi ! » — Jean Lassalle, 15 avril 2017 @HugoClement #Quotidien

10893

6437

17/04/2017 18:04

TV

TF1/TMC

qofficiel

« Si Macron passe, on pourra toujours descendre dans la rue pour gueuler. »

10174

8382

01/05/2017 17:59

TV

TF1/TMC

qofficiel

Petit rappel, en ce jour d’élection.
#JeVote #Avoté

10100

10482

23/04/2017 11:19

TV

TF1/TMC

qofficiel

Rappel : le Gorafi est un site satirique. Macron n’a pas dit que les ouvriers avaient les mains sales. @HugoClement #Quotidien

7989

4378

27/04/2017 18:31

TV

BFM TV

bfmtv

La charge de Philippe Poutou contre François Fillon et Marine Le Pen sur la morale en politique

7663

5879

04/04/2017 20:49

Figure 2. Distribution of media outlets according to network position and conversation engagement3

Figure 2

Summary

This document provides a descriptive analysis of the role of digital-born and legacy media in the flow of news content on Twitter during the French Presidential elections in 2017. We have brought evidence to understand better the re-allocation of roles between digital-born and legacy media looking at Twitter during a major political event. We have found that legacy media dominate the overall production of news content on Twitter. However, once we look at the audience engagement we have seen that Twitter activity is not always associated with the same levels of influence as measured by audience engagement. Our analysis has helped identify both the limited number of very influential, mostly legacy media, brands that figure very prominently in online political discussions in France, those that seem more influential on Twitter than their audience reach and following count alone would suggest, some who are punching below their weight, and a significant number of smaller legacy and digital-born news media organisations that see very little engagement on Twitter.

Figure 3. Sampling and filtering process

Figure 3

References

Chadwick, A. (2013). The Hybrid Media System: Politics and Power. Oxford: Oxford University Press.

Conway, B. A., Kenski, K., & Wang, D. (2015). ‘The Rise of Twitter in the Political Campaign: Searching for Intermedia Agenda-Setting Effects in the Presidential Primary’. Journal of Computer-Mediated Communication, 20(4), 363–380.

Gonzalez-Bailon, S., Borge-Holthoefer, J., & Moreno, Y. (2013). ‘Broadcasters and Hidden Influentials in Online Protest Diffusion’. American Behavioral Scientist, 57(7), 943–965. http://doi.org/10.1177/0002764213479371

Jungherr, A. (2016). ‘Twitter Use in Election Campaigns: A Systematic Literature Review’. Journal of Information Technology & Politics, 13(1), 72–91.

Morstatter, F., Pfeffer, J., Liu, H., & Carley, K. M. (2013). ‘Is the Sample Good Enough? Comparing Data from Twitter’s Streaming API with Twitter’s Firehose’. arXiv Preprint arXiv:1306.5204.

Murthy, D. (2015). ‘Twitter and Elections: Are Tweets, Predictive, Reactive, or a Form of Buzz?’ Information, Communication & Society, 18(7), 816–831.

Newman, N., Fletcher, F., Levy, D. A., & Nielsen, R. K. (2016). Digital News Report 2016. Oxford: Reuters Insitute for the Study of Journalism.

Nicholls, T., Shabbir, N., & Nielsen, R. K. (2016). Digital-Born News Media in Europe. Oxford: Reuters Insitute for the Study of Journalism.

Quinlan, S., Gummer, T., Roßmann, J., & Wolf, C. (2017). “Show Me the Money and the Party!” – Variation in Facebook and Twitter Adoption by Politicians. Information, Communication & Society, 1–19.

Stromer-Galley, J. (2014). Presidential Campaigning in the Internet Age. Oxford: Oxford University Press.

Acknowledgements

The authors would like to thank Leonie Rivere for her valuable work as a research assistant. They also gratefully acknowledge the work of Eurecat’s Digital Humanities Research Group, Andreas Kaltenbrunner, Pablo Aragón and Matteo Manca, who helped us to collect and analyse the data of this report and accommodate all our requirements. Lastly, we thank the following researchers and journalists’ contribution in helping us to design our sample of French news media: Julia Cage, assistant professor at Sciences Po Paris, Department of Economics; Arnaud Mercier, professor of communication at the University of Paris 2; Alex Vicente, culture correspondent based in France for El País; Joan Carles Peris, France correspondent, Catalan Public Television, TV3; Mariona Vivar Mompel, freelance journalist.

 

About the authors

Sílvia Majó-Vázquez is Research Fellow at the Reuters Institute for the Study of Journalism at the University of Oxford

Jun Zhao is Senior Research Fellow at the Department of Computer Science at the University of Oxford

Rasmus Kleis Nielsen is Director of Research at the Reuters Institute for the Study of Journalism at the University of Oxford

Appendix

Table A1. Reach and Twitter statistics by French media outlets

Type

Brand

TW username

Online Audience Reach

Followers

National Daily Newpaper

Le Figaro

le_Figaro

18.53

2,636,827

National Daily Newpaper

Le Monde

lemondefr

19.72

7,101,100

National Daily Newpaper

Les Echos

lesEchos

5.64

984,067

National Daily Newpaper

Liberation

libe

4.70

2,505,670

National Daily Newpaper

La Tribune

laTribune

1.96

179,558

National Daily Newpaper

La Croix

laCroix

2.25

40,617

Free National Newspaper

20 Minutes

20Minutes

10.03

2,246,457

Free National Newspaper

Cnews Matin

CNEWSMatin

0.64

14,109

Regional Newspaper

Le Parisien

le_Parisien

12.30

1,856,493

Regional Newspaper

Ouest France

ouestFrance

7.69

492,288

Regional Newspaper

La Depeche du Midi

ladepechedumidi

4.09

69,608

Regional Newspaper

Sud Ouest

sudouest

3.35

383,636

News Agency

Agence France-Presse

afpfr

0.09

2,634,211

Digital-born

Le Huffington Posts

leHuffPost

7.40

945,085

Digital-born

Slate

slatefr

2.44

658,114

Digital-born

Mediapart

mediapart

1.94

2,017,606

Digital-born

Atlantico

atlantico_fr

1.54

69,312

Digital-born

Arrêt sur Images

arretsurimages

0.14

487,942

Digital-born

Acrimed

acrimed_info

0.09

42,494

Digital-born

NonFiction

nonfiction_fr

0.03

6,112

Digital-born

Fakir

fakir_

n/a

31,228

Digital-born

cFactuel

cFactuel

n/a

3,539

Digital-born

Brut

brutofficiel

n/a

41,899

Digital-born

Rue89

rue89

n/a

1,432,801

Digital-born

Les Jours

lesjoursfr

n/a

26,829

Digital-born

Bondy Blog

leBondyBlog

0.07

30,985

Digital-born

Égalité & Récontiliation

eetR_National

0.51

30,139

Digital-born

Fdesouche

F_Desouche

0.59

24,443

Digital-born

Wikistrike

wikistrikeW

0.15

6,104

Weekly Newspaper

L’Obs

lobs

10.66

1,190,600

Weekly Newspaper

L’Express

LEXPRESS

10.24

1,193,875

Weekly Newspaper

Le Point

lePoint

5.19

641,771

TV

BFM TV

BFMTV

10.50

2,158,449

TV

TF1/LCI

LCI

5.13

94,871

TV

France 24

FRANCE24

1.37

2,770,705

TV

Franceinfo

franceinfo

13.10

1,045,639

Radio

RTL

RTLFrance

5.45

692,539

Radio

Europe 1

europe1

3.45

1,248,844

Radio

Franceinter

franceinter

2.35

1,199,497

Radio

RFI

RFI

1.35

1,447,527

Regional Newspaper

La Voix du Nord

lavoixdunord

2.33

293,432

Regional Newspaper

Le Midi Libre

Midilibre

n/a

128,142

Regional Newspaper

Le Progrès

Le_Progres

1.48

131,000

Regional Newspaper

La Provence

laprovence

1.25

174,744

Regional Newspaper

L’Est republicain

lestrepublicain

1.27

80,946

National Daily Newpapers

France Soir

france_soir

1.07

18,238

Local Daily Newspaper

Le Républicain Lorrain

lerepu

0.79

14,318

Local Daily Newspaper

La Montagne

lamontagne_fr

0.98

81,366

Weekly Newspaper

Le JDD

leJDD

0.80

321,913

Regional Newspaper

L’Union

UnionArdennais

0.63

49,629

Type

Brand

TW username

Online Audience Reach

Followers

Local Daily Newspaper

DNA Dernières Nouvelles d’Alsace

dnatweets

0.51

113,668

Local Daily Newspaper

Le Courrier Picard

CourrierPicard

0.49

86,667

Local Daily Newspaper

Lyon Mag

lyonmag

0.25

15,200

Local Daily Newspaper

La Manche Libre

lamanchelibre

0.24

6,274

Local Daily Newspaper

La République de Seine-et-Marne

LaRep77

0.24

3,564

Local Daily Newspaper

Le Populaire

lepopulaire_fr

0.27

13,782

Digital-born

Le Petit Journal

lepetitjournal

0.30

9,281

Regional Newspaper

L’Écho Républicain

lecho_fr

0.26

5,216

Regional Newspaper

L’Yonne Républicaine

lyonne_fr

0.21

6,184

Regional Newspaper

Le Berry Républicain

leberry_fr

0.27

11,023

Regional Newspaper

Vosges Matin

VosgesMatin

0.24

6,726

Regional Newspaper

L’Est Eclair

lesteclair

0.17

8,833

Regional Newspaper

Nord Eclair

NordEclairWeb

0.24

17,324

Local Daily Newspaper

Le Phare Dunkerquois

pharedk

0.10

3,782

Weekly Newspaper

Le Canard Enchainé

canardenchaine

0.20

407,141

Regional Newspaper

Le Journal du Centre

lejdc_fr

0.18

6,657

Local Daily Newspaper

Le Républicain 47

LeRep47

0.06

1,026

Local Daily Newspaper

L’Eveil

leveil43

0.06

2,900

Local Daily Newspaper

L’informateur d’Eu et L’Éclaireur

InfoEclaireur

0.05

1,038

Regional Newspaper

L’Ardennais

UnionArdennais

0.11

49,629

Local Daily Newspaper

Montceau News

montceaunews

0.09

754

Digital-born

Infonormandie

infoNormandie

0.10

8,762

Regional Newspaper

La Gazette

lagazette95

0.15

3,176

echoregional95

n/a

1,814

Digital-born

WeDemain

WeDemain

0.06

21,567

Regional Newspaper

Nord Littoral

Nordlitt

0.08

5,923

Digital-born

Mediacités

Mediacites

n/a

1,448

Digital-born

Explicite

expliciteJA

n/a

43,168

Digital-born

The Conversation

FR_Conversation

0.09

11,384

Digital-born

Brief.me

briefmenews

n/a

5,172

Digital-born

Les Moutons Enragés

Moutonenrages

0.21

4,390

Digital-born

Dreuz Info

Dreuz_1fo

0.68

12,509

Digital-born

Riposte Laïque

1RiposteLaique

0.40

9,520

Magazine

Paris Match

ParisMatch

2.84

1,116,503

Weekly Newspaper

Telerama

Telerama

3.40

334,759

Weekly Newspaper

Challenges

Challenges

1.88

196,801

Weekly Newspaper

Les Inrocks

lesinrocks

1.69

1,083,546

Radio

France Culture

franceculture

n/a

437,559

National Daily Newpaper

L’Humanité

humanite_fr

1.11

322,578

TV

CNews

CNEWS

0.19

981,305

National Daily Newpaper

L’Opinion

lopinion_fr

0.52

60,698

Weekly Newspaper

Courrier International

courrierinter

1.16

227,116

TV

LCP

LCP

0.24

370,838

TV

TF1

TF1

13.01

4,817,643

Radio

RMC

RMCinfo

n/a

125,278

Digital-born

Agora Vox

agoravox

0.74

13,379

Weekly Newspaper

La Vie

LaVieHebdo

0.27

22,140

Weekly Newspaper

Témoignage Chrétien

TChebdo

n/a

10,387

Monthly Newspaper

Capital

MagazineCapital

2.23

21,823

Source for online reach: comScore MMX Key Measures, % reach desktop only, average last 3 months, April 2017

  1.  Tweets that do not contain any of the above-mentioned criteria are not included in the final sample. Our data sampling strategy thus might have missed tweets containing native online media videos, which usually do not include an URL (to illustrate this see https://goo.gl/f439Iq). These types of tweets, which are more likely to be sent by digital-born outlets, might not have been included if they did not mention a media source on the text.
  2.  We examined the raw sample (n=43.5) to obtain this ranking.
  3.  Nodes are identified where space permits. We have used a base 10log scale for the x and y axis.