David G Cox
email: david.cox@inserm.fr

Elsa Curtit

Gilles G Romieu

Pierre G Fumoleau

Maria Rios

Hervé Bonnefoi

Thomas Bachelot

Patrick Soulié

Christelle G Jouannaud

Hugues Bourgeois

Thierry Petit

Isabelle Tennevet

David Assouline

Marie-Christine Mathieu

Jean-Philippe Jacquin

Sandrine Lavau-Denes

Ariane Darut-Jouve

Jean-Marc Ferrero

Carole Tarpin

Christelle Lévy

Valérie Delecroix

Véronique Trillet-Lenoir

Oana Cojocarasu

Jérôme Meunier

Jean-Yves Pierga

Céline Faure-Mercier

Hélène Blanché

Mourad Sahbatou

Anne Boland

Delphine Bacq

Céline Besse

Jean-François Deleuze

Iris Pauporté

Gilles Thomas

Xavier Pivot

Affiliations

GWAS in the SIGNAL/PHARE clinical cohort restricts the association between the FGFR2 locus and estrogen receptor status to HER2-negative breast cancer patients

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

/18

Introduction

Since the completion of the Human Genome Project, the Genome Wide Association Scan (GWAS) has become the tool of choice for the detection of associations between disease risk, and common genetic variation. The first breast cancer risk variants identified in the GWAS era were in the FGFR2 locus [START_REF] Hunter | A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer[END_REF][START_REF] Easton | Genome-wide association study identifies novel breast cancer susceptibility loci[END_REF].

Further analyses, mainly in case-control and prospective cohorts, have reinforced this association as well as identified over 90 additional breast cancer risk loci [START_REF] Michailidou | Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer[END_REF]. GWAS studies with cases selected based on the estrogen receptor (ER) status of their tumors, and control subjects not affected by breast cancer, have shown divergent associations between ER+ and ER-tumors. In these analyses, variants in FGFR2are more strongly associated with ER+ disease [START_REF] Siddiq | A meta-analysis of genome-wide association studies of breast cancer identifies two novel susceptibility loci at 6q14 and 20q11[END_REF][START_REF] Haiman | A common variant at the TERT-CLPTM1L locus is associated with estrogen receptor-negative breast cancer[END_REF][START_REF] Figueroa | Associations of common variants at 1p11.2 and 14q24.1 (RAD51L1) with breast cancer risk and heterogeneity by tumor subtype: findings from the Breast Cancer Association Consortium[END_REF][START_REF] Stevens | 19p13.1 is a triplenegativespecific breast cancer susceptibility locus[END_REF][START_REF] Garcia-Closas | Genome-wide association studies identify four ER negative-specific breast cancer risk loci[END_REF][START_REF] Lambrechts | 11q13 is a susceptibility locus for hormone receptor positive breast cancer[END_REF][START_REF] Purrington | Genome-wide association study identifies 25 known breast cancer susceptibility loci as risk factors for triple-negative breast cancer[END_REF][START_REF] Warren | 9q31.2-rs865686 as a susceptibility locus for estrogen receptor-positive breast cancer: evidence from the Breast Cancer Association Consortium[END_REF][START_REF] Stevens | Common breast cancer susceptibility loci are associated with triple-negative breast cancer[END_REF][START_REF] Broeks | Low penetrance breast cancer susceptibility loci are associated with specific breast tumor subtypes: findings from the Breast Cancer Association Consortium[END_REF][START_REF] Cen | Associations of polymorphisms in the genes of FGFR2, FGF1, and RBFOX2 with breast cancer risk by estrogen/progesterone receptor status[END_REF], as opposed to ER-disease, when comparing cases to healthy controls.Few single studies, however, have sufficient detail or sample size to carry out case-only analyses to further explore the relationship between genetic variants and disease characteristics, particularly with respect to amplification of the HER2 gene.Therefore analyses by subtype are often secondary, based on findings of the primary analyses of overall breast cancer risk.

Furthermore, these studies are now carried out in large consortia with the potential for heterogeneity in definitions of various case characteristics, particularly ER and HER2 status.

For example, Broeks et al. [START_REF] Broeks | Low penetrance breast cancer susceptibility loci are associated with specific breast tumor subtypes: findings from the Breast Cancer Association Consortium[END_REF] examined the association between low penetrance breast cancer loci and specific breast tumor subtypes in the context of the Breast Cancer Association Consortium (BCAC). rs2981582 in the FGFR2 locus was significantly associated with ER+/PR+/HER2-breast cancer (ncases=7201, p = 2.2 x 10 -29 ), less so with ER+/PR+/HER2+ cases (ncases=996, p=5.5x10 -4 ), and no association was observed with triple negative breast cancer (ncases=1480, p=0.841) or ER-/PR-/HER2+ breast cancer (ncases=627, p=0.396). A case-only comparison of HER2 status was carried out within ER+/PR+ and ER-/PR-groups, and neither showed any association (p=0.23 and 0.15, respectively).

In the present study, a case-only GWAS approach was used to study differences in the distribution of variants between breast cancer cases in a large, multi-center study with centralized data collection and handling, the SIGNAL/PHARE case-cohorts (NCT00381901/RECF1098). /18

Results

Genotype data was generated from 9365 SIGNAL/PHARE participants. All subjects had greater than 95% genotyping success rate. 26 pairs of individuals were identified with Identity by State (IBS) > 30%, with the subject having the most complete genotype data from each pair retained for analyses. 551 further individuals were excluded from the present study due to PCA analyses. Finally, 61 subjects with missing clinical data were excluded. A total of 8727 patients including 2516 patients with ER-breast cancer were analyzed. Furthermore, 5452 patients had HER2-negative breast cancer, of which 1185 were ER-.

The search for variants associated with ER status showed only one region with a highly significant association, corresponding to FGFR2 (best p-value for rs3135718 p-value=6.0x10 -12 , Figure 1). Restricting our analyses to HER2-negative cases found that associations between variants at the FGFR2 locus remained significant at the genome-wide level (best p-value for rs3135724 = 5.2x10 -11 , Figure 2). Among HER2-positive tumors, the lowest p-value in the FGFR2 locus for the association with ER status was found for rs2981578 (p = 3.3x10 -4 Table 1). The four variants in Table 1 were chosen to highlight the difference in associations between HER2+ and HER2-patients. Despite the smaller sample size among HER2-positive cases, this study has nearly 100% power to detect a per-allele OR =1.8 as observed among the HER2-negative tumors, and greater than 80% power to detect a per-allele OR ≈ 1.3. The observed direction of the association was consistent with observations in prior case-control studies, with for example the C allele of rs3135718 being more frequently reported among women with ER+ tumors. As mentioned previously, variants in the FGFR2 locus were the first identified via GWAS with respect to breast cancer risk. The most recent fine-mapping effort of the FGFR2 locus explored functional variants, and identified three separate independent sets of correlated highly associated variants (ICHAVs [START_REF] Meyer | Fine-scale mapping of the FGFR2 breast cancer risk locus: putative functional variants differentially bind FOXA1 and E2F1[END_REF]). In the present analyses restricted to HER2negative tumors, rs3135724 was the SNP with the strongest association for ER status. /18 These data included rs2981579 and rs2981578, from ICHAVs 1 and 3 respectively (Table 1).

Unfortunately, rs45631563 from ICHAV 2 was not included, and no SNPs showed significant linkage disequilibrium with this marker in the current 1000 genomes data (http://1000genomes.org accessed July 8, 2015). Therefore additional analyses were carried out including rs3135724, rs2981579, and rs2981578 in the same logistic regression model. In our analyses of HER2-breast cancer, we found no evidence for independent association between these variants and tumor ER status (data not shown). /18

Discussion

The identification of variants associated with specific molecular subtypes of breast cancer was a primary aim of the prospective SIGNAL/PHARE cohort. In this high-powered GWAS performed in a case-cohort of breast cancer patients with detailed clinical data, further information with respect to variants in the FGFR2 locus and their influence on breast cancer were provided, particularly regarding tumor ER status. In addition, the association between variants in FGFR2 and ER status in breast cancer was stronger among patients with HER2tumors. While not including an independent validation set is a drawback of our analyses, the large sample size allowed us to have sufficient power to fully define this association, and the p-values obtained were well below empirical estimations of significance thresholds (1.48x10 -7 ) as well as the generic GWAS significance threshold of 5x10 -8 .

Our hypothesis is that genetic variants that are associated with molecular subtypes will provide novel insights regarding disease etiology, and may lead to further developments regarding disease prevention and treatment. As our main focus was the construction of a clinical cohort, we have focused on collecting information with respect to histo-pathology and treatments, and patient follow-up. Therefore, we have not collected detailed information regarding epidemiological data such as body-mass index, reproductive history and menopausal status, or family history/BRCA mutations. The participants have been given a self-administered questionnaire with some of these variables, but as this questionnaire was administered after cancer diagnosis, we have chosen to not exploit these data at this time.

We have focused on the FGFR2 locus, which showed the strongest association with ER status, particularly among HER-breast cancer patients. There is growing evidence that genetic variants may be more strongly associated with specific breast cancer subtypes. For the most part, these analyses are extensions of current prospective cohort and case-control analyses. For example, recent analyses by Michailidou et al. [START_REF] Michailidou | Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer[END_REF] included stratification by estrogen receptor status for the 77 variants included in their polygenic risk score. A number /18 of these variants showed differential associations with respect to estrogen receptor status.

However as the authors state in their discussion, the number of estrogen receptor negative cases made accurately determining risk estimates difficult for this cancer subtype.Future analyses in our case-cohort will investigate other variants previously shown to influence breast cancer subtype.

A potential limitation of our study is the use of an internal imputation process, as opposed to imputing to the commonly used 1000 Genomes data or the Michigan Imputation Server.

As mentioned previously, this was our original study design prior to the availability of these resources. We have continued with this approach in order to avoid any potential population differences with respect to linkage disequilibrium between our population of French breast cancer cases and the populations that provided data for publicly available resources. This approach leads to a lower number of variants on the absolute scale, meaning that we may be unable to detect any additional variants not captured through genotyping with the Illumina Omni5, which captures over 80% of common variants among Caucasian populations, and strict quality filtering of data (See Methods section).

For aspects of response to treatment, SIGNAL/PHARE has not yet accrued enough follow-up to fully explore the implication of variants on patient's outcome. This will be of course an obvious next step of our analyses, particularly as pertains to response to hormone therapy and FGFR2 variants in ER+/HER-breast cancer patients.

In conclusion, we further refine the influence of variants in the FGFR2 locus with respect to molecular characteristics of breast tumors, in that they are more strongly associated with estrogen receptor status among cancers without amplification of the HER2 gene. The original study plan called for a two-staged genotyping strategy using only study participants. This approach aimed at reducing the potential that population structure in French breast cancer cases would influence imputation, while maximizing the proportion of the genome covered. Briefly, all cases were genotyped using the IlluminaHumanCoreExome chip set, composed of over 264000 variants for a "GWAS Backbone" and over 244000 "exome-centered" variants. Variants were filtered based on completion rates (<95% SNP success, N = 8122), departure from Hardy-Weinberg Equilibrium (HWE p<0.001, N = 20357), and low minor allele frequency (MAF<0.001, N=200628). Principal Components Analysis (PCA) and k-means were then used to characterize the ancestry of the participants and only the main cluster of European individuals was included in the present analysis, to reduce risk of population stratification (See Figure S1). A random subset of 1449 individuals from the main "European" cluster was selected for genotyping using the Illumina Omni5 chip set, composed of over 4M variants (See Figure S1). Complete (SNP success = 100%, N=2049173) Omni5 data were then filtered using similar cutoffs as the HumanCoreExome data, specifically HWE (p<0.001, N=91018) were then used to impute 1 1/18 missing genotypes from the remaining subjects genotyped using the HumanCoreExome array. SNPs with imputation quality score < 30% were excluded from analyses (N=783416), and finally variants with a MAF < 0.01 were excluded (N=82847). A total of 914144 SNPs were included in the GWAS analyses. Standard GWAS logistic regressions were carried out using the ProbABEL package [START_REF] Aulchenko | ProbABEL package for genome-wide association analysis of imputed data[END_REF]. Age at diagnosis and the first two principal components were included in regression analyses.

Genome-wide significance levels were estimated using the effective number of tests based on linkage disequilibrium between all markers used in our population through the SimpleM function in R [START_REF] Goa | Avoiding the high Bonferroni penalty in genome-wide association studies[END_REF]. The number of effective markers is estimated at 345906, corresponding to a Bonferroni-corrected p-value threshold of 1.48x10 -7 .

1 0/ 18 Methods

 18 PHARE was a randomized phase 3 clinical trial comparing 6-and 12-month trastuzumab adjuvant exposure[START_REF] Pivot | 6 months versus 12 months of adjuvant trastuzumab for patients with HER2-positive early breast cancer (PHARE): a randomised phase 3 trial[END_REF], which included a subset of 1,430 HER2-positive breast cancer cases with DNA available for GWAS analyses. SIGNAL was a prospective cohort specifically designed for GWAS analyses of 8,406 early breast cancer patients, enrolled at the time of the adjuvant chemotherapy from June 2009 to December 2013. The combined data set, the PHARE/SIGNAL study, included 9,365 breast cancer patients. Clinical and pathological characteristics were prospectively collected using standardized forms, and centralized at the French National Cancer Institute (INCa). For both studies, patients provided blood samples that were centralized at the Centre d'Etude du PolymorphismeHumain (CEPH) in Paris, France, for DNA extraction using standard protocols. Genotyping was carried out at the Centre National de Génotypage (CNG) in Evry, France.

Table 1 . Selected variants at the FGFR2 locus and ER status among breast cancer cases

 1 .16 -1.33) 5.5x10-11 1.15 (1.03 -1.27) 9.2x10-3 1.33 (1.20 -1.47) 2.1x10-9 * Imputed (I) or genotyped (G). Values reported from MACH output

	Overall	HER2+	HER2-

Acknowledgements

The authors would like to thank the patients who took part in the study as well as medical staff for their cooperation. We also would like to thank Nicolas Thammavong for his contribution in clinical data management, Alexia Renoud for her contribution in clinical data management and descriptive statistics analyses, and the CEPH Biological Resource Center and CNG genotyping staff for technical assistance.

Conflict of Interests

The authors have no conflicts of interest to declare. shows the strongest association. The same 914144 SNPs were included in these analyses, with 4267 HER2-/ER+ and 1185 HER2-/ER-cases. The red horizontal line corresponds to the empirical significance threshold of 1.48x10 -7 , while the blue horizontal line corresponds to an arbitrary level of 1.0x10 -5 . The inflation factor (λ) for these analyses is 1.02.

Grant Support


Gilles Bastin
email: gilles.bastin@iepg.fr

Milan Bouchet-Valat

R Temis

A Free Software Text Mining Approach to the Framing of Julian Assange by Three News Agencies Using

Keywords: Text mining, R, media studies, correspondence analysis, geographical mapping, framing, news agencies, Assange

In this paper, we introduce R.TeMiS, a free software text mining solution aiming at exploring new dimensions in text mining with a particular focus on media framing analysis. R.TeMiS is especially designed to provide help in a) the automation of corpus construction and management procedures based on the use of large media content databases and b) the extension of the range of statistical tools available to social scientists exploring texts through R coding (one-and two-way tables, timelines, hierarchical clustering, content analysis, geographical mapping…). A case study on the media framing of Julian Assange from January 2010 to December 2011 is conducted.

Introduction

The methodology of applying statistics to large-n text corpora 1 -which we will here refer to as « text mining »-has gained legitimacy in every field of the humanities and social sciences (HSS) since the 1960s. 2 Many factors have contributed to this methodological shift. The rise in computer literacy among scholars, the diffusion of standard textual data analysis tools in surveys (Lebart et al., 1998) as well as the availability of software solutions based on graphical user interfaces providing such tools have notably diminished the costs of learning in this field. Simultaneously, significant changes have occurred in the way that social sciences access and store textual data. The digitalization of text corpora in many fields of social activity has for instance produced a deluge of available new documents to the social scientist, a phenomenon that has been referred to as a « data deluge » (Abbott, 2000; Hey and Trefethen, 2003).

Due to the digitalization of newspaper archives on the web, the availability of articles on platforms such as Lexis-Nexis or Factiva, the slow but growing textualization of audio-visual media, scholars working in media studies have particularly benefited from this new abundance of research materials and promising shift in methodologies. 3 Conventional content analysis -a method consisting of sampling newspaper archives and assigning abstract categories to passages of these articles in order to measure the recurrence of « themes » or « issues » within that content -has experienced a decline in this field of research. 4 It is increasingly challenged by new corpus-driven text mining techniques based on an exhaustive analysis of digitalized corpora extracted from databases, using keywords searches to select the material and inductive methods based on semantic (instead of thematic) fields identification within the corpus (frequency, concordances, collocation analysis). 5 Unfortunately, the evolution of the software market has produced an abundant, intimidating and often obscure « maquis » of technical solutions for scholars intending to experiment with text mining methods on media corpora (Demazière and Brossaud, 2006). The average user must choose between dozens of applications. Moreover, each tool tends to be conceived as a black box: an integrated solution providing help from the corpus management to the edition of analysis reports, but also -and for that same reason -a world apart with its own peculiar customs, vocabulary and paradigms. As a consequence, text mining often appears as a very complex and clustered methodology, reserved only for those who share the theoretical paradigms behind the software they use. This black box phenomenon obviously has some advantages when a researcher knows his box (the diminution of learning costs is one). But it clearly has also many disadvantages when it comes to the availability of such methods for researchers, the comparability of research results and the issue of controlling one's work in a way that helps, rather than hinders, the « sociological imagination » (Demazière, 2005; Mills, 1959). We strongly believe that using text mining as a means to test hypotheses and generate new interpretations of the social world-the « back-and-forth between sociological questioning and testing of 1 By large-n text corpora we do not only mean text corpora containing a lot of words (like novels for instance) but also corpora made of a lot of different documents (like corpora made of hundreds of newspaper articles for instance). 2 In France for instance it has been acknowledged as a legitimate methodology in language science (Muller, 1969), history (Guerreau, 1989) and sociology. See for instance the preface of Baudelot to Lebart and Salem (1994). 3 As have scholars working with open-text responses to surveys (Bolden and Moscarola, 2000), scholars in the field of linguistics (Sinclair, 2004; Tognini-Bonelli, 2001) and discourse analysis (Baker, 2006). 4 See Berelson (1952) for the classical presentation of conventional content analysis and de Bonville (2000) for a recent survey. Interesting methodological discussions on conventional content analysis can be found in Krippendorff (2004a; 2004b) and Krippendorff and Bock (2008). 5 For an interesting discussion of the methodological issues of corpus-driven content analysis, see Schafraad (2006) algorithms » (Demazière and Brossaud, 2006)-requires software solutions that can be fully controlled by users, rather than black boxes. 6 The R statistical framework (R Core Team, 2013) has long been acknowledged to empower the social scientist with control over his or her work in every kind of statistical research. Thanks to the tm package (Feinerer, 2008; Feinerer et al., 2008; Feinerer, 2011), but also to other packages dedicated to advanced text mining operations as well as to general purpose packages which can be used for this particular application, R is a very fruitful environment for text mining. Yet, the power and the flexibility of this environment include the disadvantage that beginners can become confused due to the lack of a graphical user interface in the original R software (a feature that is especially interesting in text mining, in particular because text mining requires frequent close examination of the original corpus to contextualize statistical results).

In this article, we present R.TeMiS, a new R package that intends to fill this gap by providing users with a graphical interface (Bouchet-Valat and Bastin, 2013). 7 Even though R.TeMiS is a general purpose text mining tool, we will focus in this article on its use in media studies. The argumentation will thus be illustrated with a study on the media framing of Julian Assange from January 2010 to December 2011. This study is based on the analysis of a corpus of 667 news dispatches published in English by the three top international news agencies: Agence France Presse (AFP), Reuters and Associated Press (AP). 8 We do not aim here at a comprehensive analysis of this corpus but only to provide the reader with a good overview of R.TeMiS's possibilities using a real case study of media framing issues. 9 This case study has been chosen because the name of Julian Assange was associated at that period with competing « frames » in the news: the whistleblower who initiated the Wikileaks public scandals was one; the sexual offender who was facing a private scandal another. 10 Moreover, analysis of dispatches offers a good challenge for text mining tools. Because news agencies provide news to other media outlets (newspapers, TV stations, radio, websites, etc.) they are essential regularities of content which record and reflect objective mechanisms of a social order ». Therefore, they need complete and reflexive control over their methodological tools, meaning the ability to easily gather and process big corpora, apply robust statistical methodology to those corpora, and to manipulate contextual variables for understanding the contents and easily use the data for a range of new treatments. « The classical role of cultural scholarship as a testing ground of critical social theory, Gerbner concluded, is to be strengthened, broadened, and deepened -not abolished -in the analysis of mass media content through the newer, more systematic and refined methodologies » (Gerbner, 1958). 7 R.TeMiS has been developed as a specific menu of the R Commander, a well-known and very robust GUI for R (Fox, 2005). For that reason, the package name in R conforms to the Rcmdr plugins syntax («RcmdrPlugin.temis») but can be shortened as R.TeMiS (for R Text Mining Solution). R.TeMiS also refers to Artemis, the sister of Apollo, a goddess who was highly venerated in ancient Greece. She was often depicted as a huntress wandering in forests, uncultivated areas and other wild lands. Instructions to download and launch R.TeMiS are available at the following URL: http://rtemis.hypotheses.org/. For a general overview of the package's features, see Bouchet-Valat and Bastin (2013). 8 The search was made on Factiva, a Dow Jones company providing access to media contents. Every dispatch containing the term "Assange" in the headline or 1st paragraph was retained. 9 News «framing» has been widely studied since the 1980's. The paradigm has many possible interpretations and has even been called a « fractured paradigm » (Entman, 1993). We will use « frames » here in a very minimal and commonly shared sense as « the central organizing idea or story line that provides meaning to an unfolding series of events » (Gamson and Modigliani, 1987: 143). For a further review of the framing literature, see De Vreese (2005). 10 Julian Assange co-founded Wikileaks in 2006. Since then, the website has specialized in leaking secret files such as the famous Afghan and Iraqi « war logs » and secret US cables in 2010. Julian Assange has received various prizes for his contribution to freedom of the press and was elected Person of the Year by Time magazine and Le Monde in 2010. The same year, the site began to be targeted by the American government, which succeeded in blocking its financial resources (Visa and Paypal). On the 20th of August 2010, Julian Assange was accused of sexual offenses in Sweden. On the 8th of December 2010 he was arrested in Great-Britain, then was granted bail after the payment of 240.000 £ in cash and sureties. In June 2012 Assange violated the conditions of his bail and applied for political asylum at the Ecuadorean Embassy in London.

actors in the early framing of social and political problems. At the same time, since they do it in very routinized forms-in very « objective » forms to use the journalistic vocabulary-their coverage of breaking news stories like the one we study is very similar. Thus, identifying the frames they produce requires precise scrutiny of the vocabulary employed in their dispatches. Briefly, elucidating framing issues in news agencies' coverage of topics like Julian Assange is a good way to test the ability of text mining tools to identify small but meaningful differences in large-n corpora that are easily constituted from existing electronic sources. This facilitates discovery of what contemporary media studies can expect from text mining.

This paper is composed of six sections. In the first section, we highlight three reasons to choose free software in the field of media corpora text mining. In the second section, we introduce the importation, coding and management of media text corpora in R.TeMiS. In the third section, we illustrate the package's variables visualization features. The fourth section is devoted to elementary statistics with R.TeMiS. The fifth section addresses hierarchical clustering and correspondence analysis. The sixth section provides an illustration of the advantages of R for extending the range of statistical procedures that are currently available (with a focus on geographical mapping of term frequencies).

Advantages of a free software text mining approach to media studies Opting for free software or open-source statistical solutions has not only to do with broad preferences for costless software solutions. It has also to do with workflow choices that have an effect on the results produced. Three main reasons can be identified for preferring an open-source approach: free cost, robustness and reusability.

The first advantage of open-source text mining is free cost. Most available text mining software solutions are sold at a high price point, whether as stand-alone processing solutions or as add-ons to general purpose-and expansive-statistical software solutions.11 This is clearly a limitation for their use, and obviously presents difficulties for researchers without access to financial resources (not to mention graduate and PhD students or researchers in emerging countries). Free cost also presents another advantage. Whereas proprietary software solutions tend to be arbitrarily limited in their processing capacities (according to the kind of license paid), free software solutions do not represent such limits outside the user's hardware processing capacity and time available (of course very big corpora can induce a very long calculation time). Proprietary text mining software solutions typically tend to limit the maximal size of corpora, sometimes with different limits according to the license paid. This is a big issue in media research where corpora can be significant in size. This is evident in situations where researchers adapt the size of their corpus to fit the software's own limitations, rather than research questions.

Another interesting feature of open-source text mining is robustness. Most packages in R have been created by very experienced researchers in their field. They are constantly scrutinized and improved by users' communities. In contrast, closed source text mining software solutions do not benefit from this kind of community-driven improvements. Each proprietary text mining application must include its own implementation of fundamental statistical methods. This results in a risk of introducing bugs that can go unnoticed since the code cannot be checked and the same analysis cannot always be performed using different programs to compare results.

The third key feature of free software text mining is reusability. Integration with a free general-purpose statistical framework such as R means that all the methods developed for this environment are made available to text mining. By default, R.TeMiS makes use of many free software packages 12 , which have in some cases been modified by their authors to better suit their needs. Even more features are offered to advanced users by giving them access via manual code edition to any method they want. This is congruent with the spirit of free software, and contrary to proprietary software solutions that do not allow users to modify software code to fit specific scientific needs (outside the usual parameters toolboxes). R.TeMiS thus gives the user full control over the code producing the analyses. Following the general principle adopted by the R Commander, the commands generated by the user's actions in dialog boxes are printed in a script box and can be edited and run as plain code. This allows users to check what is being done by the software and optionally extend standard analyses with custom R commands most fitted to their needs.

Importing, coding and managing large-n media corpora with R.TeMiS An important feature of the R.TeMiS approach to media studies is to facilitate the constitution of media corpora. Handling and coding corpora is a key moment in media research. Yet, too often, it is considered only as a preliminary step, and not part of the analysis. Text mining solutions typically require the researcher to produce specific types of documents (or files) with a specific formatting that can only be obtained manually. Consequently, researchers have to perform operations such as copy/paste chunks of text in order to transform many documents into one (e.g., many newspaper articles pasted in one single document in a chronological order), insert code lines within the corpus to introduce contextual variables (e.g., date of publication, source, author), suppress certain undesired recurring words, or expressions that hinder the analysis (such as the title of the source media).

The disadvantages of such handmade corpora are evident. The first is the vast amount of time needed to constitute the corpus. Time considerations may compel researchers to outsource this analytical step and thus, at least partially, surrender control. A second disadvantage is the high probability of error during manual manipulation of the corpus (e.g., deletion of documents, errors in automatic replacement formulas, etc.) or losing track of the changes performed (because users modify their source corpus, changes cannot be undone unless a very precise record has been kept). Last but not least, such corpora produce locked-in effects due to dependency on the software used. 13 The integrated approach that has been adopted in R.TeMiS consists of providing the user with corpus importation filters that allow the use of many kind of corpora without customized intervention by the researcher. These filters simplify the characterization of the contents with relevant metadata (date, author, source, etc.) by automatically retrieving variables when they are available (as is often the case in structured media contents databases) or by allowing the researcher to document his corpus without modifying it. Whenever necessary the R.TeMiS package provides tools for traceable corpus changes from within the software (i.e. without manually modifying the corpus).

To do so, R.TeMiS supports different kinds of structured source files corresponding to various kinds of research materials: 12This is notably the case for stemming and, for corpora importation, but also for graphics, which are usually very poor in proprietary solutions but of very high quality in R. 13 For example, when variables are added manually within the corpus, using a syntax that is specific to the software (thus making it difficult to perform different kind of analysis without recoding the corpus and then facing the waste of time and high probability of errors issues).

• series of plain text files (.txt) contained in a directory (typically the result of audiovisual media materials transcriptions or a sociological interviews campaign);

• spreadsheet-like files (.csv, .ods, .xls) with one line per document/person. The first column containing the text to analyze and the remaining columns providing information about the document (typically the answers to a free-form question in a survey research or tabulated lists of newspaper articles containing columns for text and relevant variables);

• structured files (.xml or .html) exported from the Dow Jones Factiva content provider (typically the result of a media content analysis process using keyword search);

• Twitter searches on hashtags, authors or full text, or more complex queries using the Twitter API. 14 The Assange corpus has for instance been downloaded from Factiva as a series of seven structured .html files containing dispatches by bunches of one hundred. All files have been saved in one directory on the computer. The R.TeMiS 'Import corpus' dialog box (Fig. 1) is used to process the documents (the dispatches). This action produces the document-term matrix (DTM) that is the basis of all further statistical treatments. Documents can be split into smaller ones that are defined as a number of adjacent paragraphs (every dispatch would then be separated into many documents). This feature can be useful for relatively long and potentially heterogeneous documents. 15 Documents can also be processed to make them more suitable for statistical analysis : conversion of the text to lower case, punctuation removal, stopwords removal, and finally stemming that is carried out using language-specific algorithms derived from the work of Martin Porter. 16. This processing option is most interesting for small corpora since slight grammatical variations can then reduce the number of co-occurrences between two documents. While importing the corpus, R.TeMiS automatically retrieves variables from the Factiva .html files and asks the user which should be retained. Only two are kept in the following: the Origin (AFP, AP or Reuters) and the Date (in a YY-MM-DD format).

14 Many other corpus importation filters can be developed pertaining to every user's need. R.TeMiS automatically retrieves variables in the case of spreadsheet files, structured Factiva files and Twitter searches. For plain text files, the user only has to fill variables information for each document or provide the R Commander with a spreadsheet-like file with one row per document (identified by its order) and one column per variable. 15 Corpus heterogeneity can stem from complex factors: in some cases keyword searches can produce artifacts when the same term refers to very different things; media contents can also be indexed strangely in databases, such as grouping all « News in Brief » kinds of articles together, regardless of subject. Splitting documents in smaller ones based on paragraphs and choosing only the paragraphs containing specific terms can help to solve this problem. 16 These algorithms are provided by the Snowball project, cf. http://snowball.tartarus.org/, and used in R via the SnowballC package (Bouchet-Valat, 2013). Stemming is currently supported for the following languages: Danish, Dutch, English, Finnish, French, German, Hungarian, Italian, Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish and Turkish. After this step, a summary of the DTM is printed on the screen (see Fig. 2). The number of documents (lines of the DTM) and the number of terms (columns of the DTM) are printed, as well as a measure of the matrix sparsity (% of cells with zero occurrences) and the weighting unit. Later analyses are performed using the 'Text mining' menu in the Rcmdr window.

Visualizing relationships among variables within the corpus: the time and source structure behind the coverage of the Assange case Media analysis often requires the manipulation of contextual variables describing the documents before launching textual data exploration within documents. For that purpose, R.TeMiS provides users with meta-data visualization tools such as one-and two-way tables using meta-data variables (with optional plotting of the results). Plotting the number of dispatches produced by each news agency is interesting for the Assange case (Fig. 3). This plot clearly shows that AFP devoted far more coverage than Reuters and AP to Julian Assange in 2010-2011. Whether this is due to editorial choices putting Assange on the media agenda more often or artifactual factors linked to different dispatches publication policies is of course impossible to say without further qualitative analysis of the corpus. However, as far as the raw number of dispatches is concerned, AFP clearly gave more visibility to Julian Assange than did its competitors. The same menu also makes it possible to plot time series representing the number of documents over time, using a single curve, or one curve for each level of a variable. A rolling mean can be computed over a configurable time window. 17 The Assange corpus already contains date information imported from Factiva, which is very useful in studying media cycles around this subject. For example, the two following figures clearly show a concentration of media attention in December 2010, when Assange was arrested in Great Britain, and not in August when the sexual assault case began in Sweden (Fig. 4 shows daily counts whereas Fig. 5 A more conventional two-way table representation of the media chronology using a recoding of the Date variable to monthly breaks instead of daily ones is also very useful to assess the level of episodicism of every news agency. 19 As can be observed in Table 1 below, Reuters demonstrated the most concentrated time structure among the three agencies. The coverage for December 2010 (Julian Assange's surrender to the London police and granting of bail by the High Court) and February 2011 (a District Judge in south London ruled that Assange should be extradited to Sweden) accounted for 65% of the overall coverage (54% for AFP and 42% for AP). AP seemed to provide the most consistent coverage. Identifying media frames through elementary corpus statistics

Media frames can be identified in a corpus by finding those terms that are very specific of a given level of a contextual variable -i.e. terms whose observed frequency in each level is either too high or too low compared to what would be expected given the documents' lengths and the global distribution of terms in the corpus. If we consider for instance the three different sources gathered in the Assange corpus, some meaningful differences arise from specific terms analysis (see Appendix 1). 20 The use of « whistleblower » for instance is very specific to AFP (referring to Assange's role as leader of Wikileaks) and is specifically absent from the two other sources. The different terms used to describe the alleged rape (« assault » and « rape ») were specific to AFP in contrast with « offense » (specific to AP) and « misconduct » or « crime » (specific to Reuters). Some secondary stories within the Assange story also seemed to have stronger connections with a specific other agency : AP for instance showed significant use of « Mayawati », the name of Uttar Pradesh's chief minister who was involved in the Cablegate leak. Reuters significantly mentioned « Elmer », the name of a former Swiss banker who collaborated with Wikileaks and even gave a press conference with Assange in January 2011. This interestingly suggests that press agencies have some autonomy in choosing angles and framing what happened when compared to the « primary definition » (Hall, 1978) performed by the Swedish prosecutor Marianne Ny. The fact that her decision was written in Swedish of course opened up space for interpretation, notably of the Swedish term « sexuellt ofredande » that can be translated many ways.

Instead of focusing only on statistically specific terms, researchers working in media studies can preferentially examine only those terms that are relevant to their research question and hypothesis. Indeed, such terms can be sociological artifacts (like the very specific occurrence of press agencies acronyms in their dispatches), and with large corpora there are so many significantly specific terms that the researcher can-not analye all of them. R.TeMiS offers interesting features for a deductive approach based on analysing terms chosen by the researcher as well. The previous measures can be computed for chosen terms by levels of a variable and a plot can be drawn (see Appendix 2 and Fig. 6 for an application of terms used in the accusations against Assange). For instance, the terms « molestation », « molested », « molesting »stemmed as « molest » -weren't specific enough to appear among the 25 most specific terms in the previous treatment. They can still be considered interesting because they are closest in meaning to the terms used by the Swedish prosecutor. 21 This analysis strongly contrasts with AFP and Reuters which did not use a similar vocabulary to describe the charges. An inductive approach to media framing using correspondence analysis Hierarchical Clustering (HC) and Correspondence Analysis (CA) are two very popular ways of handling text corpora and trying to reveal their structure directly from the kind of vocabulary employed. But performing such analysis with big media corpora that has been automatically retrieved from databases or web archives induces some methodological peculiarities due to various sources of heterogeneity and redundancy within the corpus. R.TeMiS provides many computing and visualizing parameters that have been designed to help the user create the best possible representation of his corpus (not taking into ac-21 Other measures can be computed by R.TeMiS, both by document and by levels of a variable: vocabulary summary (number of terms, diversity and complexity) ; co-occurring terms (terms that tend to appear in the same documents as a chosen term); dissimilarity table (Chi-squared distance between row profiles of the document-term matrix collapsed according to the levels of a variable).

count the unlimited possibilities offered by a direct access to the R code). In what follows CA will be used to illustrate an inductive way to identify frames within the Assange corpus. 22 A first issue in large-n Factiva corpora is the artifacts created by the presence of irrelevant terms in parts of the corpus. In the Assange corpus it appears that the recurrence of the term « ID » in Reuters documents (a reference to other dispatches identification number within the database) produces artifactual results that are very obvious in the first CA produced if one does not intervene on the corpus. Previously excluding the term is of course the best thing to do. When dealing with large corpora, it can also be very useful to limit the number of terms taken into account and thus dramatically reduce the computing requirements of CA and HC in terms of both memory and time. R.TeMiS thus offers an option to exclude terms that are not present in more than a given percentage of the documents (sparsity level). For instance, using a 96% sparsity parameter leads to omitting terms absent from more than 4% of the documentsmeaning 27 in the Assange corpus-in the DTM. The user can also limit the number of terms plotted on a CA graph to the most contributing ones. 23 Fig. 7 shows the result of a CA conducted on the Assange corpus (with deletion of all terms mentioned in note 18), a 96% sparsity parameter and selection of the 30 most contributing terms to both axes only. The first plane of this correspondence analysis, despite the low level of total variance explained (which is due to the large amount of information contained in a large corpus), highlights the presence of three main media frames influencing Julian Assange's media coverage during the studied period: the Swedish « rape » case and (essentially) its British and Swedish judiciary consequences (the ten most contributive terms on the positive side of axis 1 are « court », « appeal », « swedish », « extradit », « sweden », « lawyer », « judg », « hear », « rape », « suprem ») ; the Wikileaks context is behind the most contributive terms in the top left quadrant (« document », « war », « pentagon », « afghan », « man », « civilian », « iraqi », « militari », « classifi », « afghanistan ») ; the financial surroundings of the Wikileaks issue and the attempts of the American administration to block Wikileaks' financial resources is suggested by the most contributive terms in the bottom left quadrant (« attack », « mastercard », « visa », « bank », « paypal », « anonym », « cyber », « payment », « swiss », « compani »). 24 This third frame would have been very hard to identify in the corpus without fine tuning of the sparsity permitted by R.TeMiS. 23 R.TeMiS users can also decide to restrict the corpus to a specific subset of documents, by retaining only those containing (or not containing) some terms, or those corresponding to the given level of a variable. Because « documents » can either mean the initial text documents (here the dispatches) or text chunks within these documents (if this option was checked during the import process), thematic corpora can be created from large and heterogeneous original corpora: a selection of chunks (for instance paragraphs) according to a set of terms contained in these paragraphs can help focus the corpus. 24 While showing the results of a CA, R.TeMiS opens a new window containing summary information on the plotted axes: percentage of inertia, most contributive terms on the positive and negative side, most contributive documents on each side (with the text contained in these documents), and position and quality of representation of the variables on the axis. This is very useful to interpret the CA planes.

News agencies do not really differenciate on this first plane. When plotted as levels of a supplementary variable, they are located right in the middle of the first plane. A way of trying to find differences between agencies is to produce a new CA based on a version of the DTM aggregated by the levels of the Origin variable, a feature that is proposed in R.TeMiS's CA menu. This classical method allows imposition of an interpretation framework defined a priori, making more apparent the differences that are of interest for the question at hand.

The first axis of this new CA clearly opposes AFP on the negative side to Reuters and AP on the positive side (see Fig. 8 below). AFP contributes to 45% of the construction of this axis and has a 100% representation quality on it. AP contributes to 30% of the axis construction and has a 47% representation quality.

Reuters contributes to 25% and has a 42% representation quality. 25 Terms that stand on the negative side of this axis with AFP are « whistleblow » (3.09%), « assault » (1.68), « organis » (1.04), « websit » (0.97), « warrant » (0.82), « rape » (0.78), « claim » (0.76), « australian » (0.76), « dollar » (0.72) and « enrag » (0.69). In addition to translation choices concerning the facts (« rape » and « assault ») and the already mentioned use of « whistleblower » to describe Assange and his « website » and « organisation », these terms express the pressure directed at Assanges by the judiciary process (« warrant » but also « arrest » (0.66)) and a personal account of his situation with the use of references to his being Australian. His family (« son » 0.63) and « mother » (0.48) appear on this side of the axis due to dispatches mentioning statements made by his mother concerning various aspects 25With only three news agencies, only two CA axes are by definition required to describe 100% of the variance.

of the case. « dollar » has been used by Assange himself to blame the US for the losses in financial support to Wikileaks following the funding blockade. AFP also published numerous dispatches mentioning that Assange had « enraged » US authorities by releasing classified documents.

AP and Reuters are positionned on the positive side of axis 1 with the following contributive terms : « sex » (2.33), « misconduct » (2.00), « organ » (1.76), « assang » (1.23), « investig » (1.17), « volunt » (1.17), « offens » (0.97), « defens » (0.79), « offici » (0.78). The two agencies are connected to a different frame concerning the facts. The terms chosen to describe them differ with an emphasis on the sexual nature of those facts and a more moralistic perspective (« consensual », « misconduct », « offense » but also « wrongdoing » (0.52)). « Volunt » stands for « volunteers » and describes the status of the two women inside the Wikileaks organization. The legal procedure is also highlighted with references to « investigations » and Assange's « defense ». By comparison with the personal tone used on the negative side of this axis, here « officials » (and « official » statements) are more present. (1.30), « rape » (0.98), « spokesman » (0.83), « claim » (0.73). In addition to a clear focus on Sweden, this side of the axis is more concerned with the sexual case and expresses a formal use of objectivity « rituals » (Tuchman, 1972) like mentioning sources and using quotes (« spokesman », « claim », « document » (0.67), « say » (0.61). « Pentagon » also appears there, which is congruent with AP's national origin.

Fig. 9 -CA on DTM aggregated by Origin (terms most contributive to axis 2)

Mapping Julian Assange?

Due to the fact that R.TeMiS edits plain R code at every step of the lexical analysis, it enables using this code to extend the range of statistical procedures applied to the document-text matrix beyond classical text mining. Every kind of statistics or visualization method can be applied to the DTM. The Assange corpus offers a good example of this feature. The framing of Assange by the three news agencies entails a geographical dimension: media frames, in this case, are also about drawing a world map to portray Assange. The rape case for instance mostly connects Assange to Sweden and Great Britain. Other frames such as the Wikileaks context connect Assange to other parts of the world: Australia where Assange was born, Iraq and Afghanistan referring to Wikileaks' massive leaks of classified information in 2010, the United States due to its determination to stop Wikileaks from operating, etc. Plotting a world map of the Assange case according to the three agencies provides an interesting way to visualize this corpus. 29 Frequencies of terms referring to a country can of course be tabulated within R.TeMiS. After identifying those terms using the 'Terms dictionary' menu (a menu that is also useful to check how words have been stemmed), their frequencies can be computed using the 'Descriptive analysis of vocabulary ➝ Analysis of chosen terms...' menu. Appendix 3 contains the results of this analysis for each level of the Origin variable. 30 Some interesting things appear clearly in this table such as the importance of references to Sweden (« sweden », « swedish », « stockholm ») and Great Britain (« britain », « british », « london », « england », « english », « uk ») that exceed every other geographical denomination and exceed 0.3% of the occurrences at least one news agency. The only other term that shows the same level of frequency is « australian ». The t-values can be used in these tables to identify significant over-and underrepresented terms in the three agencies. The three terms referring to Sweden are for instance under-represented in AFP (that strongly contrasts with AP on this point). To the contrary, AFP makes significant use of terms referring to Australia-hence to Julian Assange's biography-in comparison to both AP and Reuters.

These tables have strong limitations. The first involves the various terms used to refer to the same country (country names, city names and adjectives). The second involves the fact that comparisons are hindered by the production of three different tables instead of one. A little piece of R code, written directly in the Rcmdr script window, can help by constructing groups of terms and calculating frequencies for these groups. In Table 2 for instance, terms have been grouped using two-digit country names as level. 31 29 Thinking that way when studying international news agencies is also interesting because news agencies are often suspected of being biased according to their national origin. 30 Every geographical term with more than 20 occurrences in its stemmed form in the whole corpus has been retained. Terms are ordered by country. 31 The code is the following : getPercents <-function(terms) colSums(rbind(termFrequencies(dtm, terms, meta(corpus, "Origin")[[1]])[, 1,])) countries <-list(SE=c("sweden", "swedish", "stockholm"), UK=c("britain", "british", "london", "england", "english", "uk"), AF=c("afghanistan", "afghan"), IQ=c("iraq", "iraqi", "baghdad"), AU= c("australia", "australian"), US=c("america", "american", "washington", "york", "usa"), IC=c("iceland"), RU=c("russia", "russian"), CH=c("switzerland", "swiss"), DE=c("germani", "german", "berlin"), FR=c("franc", "french"), IR=c("iran"), PK=c("pakistan"), EC=c("ecuador"), ES=c("spain")) Small differences can be observed in Table 2 in the way that news agencies mapped the Assange case.

When compared to its European counterpart Reuters, AFP highlighted countries such as Afghanistan, Iraq, Australia, Iceland, Russia, France, Pakistan, Ecuador and Spain. But it relatively under-reported for Sweden, Great Britain, the US and Germany. AP over-reported the U.S. as could be expected but underreported Great Britain (on the contrary, Reuters over-reported both the US and the UK). A little R coding can help to geographically plot these data. R provides packages, like the maps package (Becker et al., 2013), that can easily draw maps and plot points using standard geographical coordinates. Transforming Table 2 into a data frame and adding two new variables (a latitude and a longitude for each country) is all we require. It can be done manually but the risk of making errors would then be significant. Instead, finding a data frame with country centroïds coordinates and importing it in R to merge it with the frequencies data frame is a better option. 32 Figure 10 represents the resulting maps (the areas of circles are proportional to frequencies). It helps in figuring out the geographical dimension of the way news agencies framed the Assange case. Similarities between the three agencies are very striking. No alternative geographical framing occured during the two studied years outside the episodic frame focusing mainly on Sweden and the UK, secondarily on the US and Australia and only thirdly on other countries mostly connected to Julian Assange due to various Wikileaks operations (Iraq, Afghanistan, Switzerland, Russia, Germany, etc.). But when looked closely at those maps slightly differ. For instance, over-reporting of agencies' origin countries is observable, as well as some peculiarities such as the importance of references to Iran in Reuters' dispatches, Pakistan in AFP's ones, etc. 33 32 The data frame used here is freely available for download at http://gothos.info/resources/. Many other data frames containing geographical data (city coordinates for instance) can be found easily on the Web. 33 The code is the following (this requires code from note 27 to be run first) : # Creation of a data frame containing all country frequencies # (countries being designated with the two-letter ISO 3166 code that is used in the country centroids file) freqs <-as.data.frame(freqs) # Simplification of agency names names(freqs)[1] <-"AFP" names(freqs)[2] <-"AP" names(freqs)[3] <-"Reuters" # Importation of the country centroids file (previously downloaded on the hard disk) centroids <-read.delim(file="country_centroids_primary.csv", header=TRUE) dat <-merge(freqs, centroids, by.x="row.names", by.y="ISO3136") # Drawing of the three maps library(maps) map(database="world", col="grey") title("Agence France Presse") points(dat$LONG, dat$LAT, pch=21, cex=10*sqrt(dat$AFP), bg="#00000040") map(database="world", col="grey") title("Associated Press") Fig. 10 -Mapping J. Assange in the three agencies points(dat$LONG, dat$LAT, pch=21, cex=10*sqrt(dat$AP), bg="#00000040") map(database="world", col="grey") title("Reuters") points(dat$LONG, dat$LAT, pch=21, cex= 10*sqrt(dat$Reuters), bg="#00000040") world agencies. 34 This of course is an interesting result if one looks at broad differences in the way that news is shaped at the global level for events with a clearly episodic nature. But it is also very interesting to notice that using sophisticated tools with fine tuning options can help to identify differences in the dispatches of the three agencies. These small differences tend to oppose AFP on one side and mostly AP on the other. The French agency tended to be less concerned with the legal procedure and the sexual case and more with the geopolitical and financial aspects of the whistleblower's situation. It also provided a more personal description of Assange, relying less on official reports and more on accounts by his relatives.

We

Fig. 1 -

 1 Fig. 1 -The 'Import corpus...' toolbox while importing the Assange corpus

Fig. 3 -

 3 Fig. 3 -Coverage of the Assange case by the three agencies

  Fig. 4 -A timeline of all documents in the Assange corpus by source

Fig. 6 -

 6 Fig. 6 -Framing the facts in the Assange corpus

Fig. 7 -

 7 Fig. 7 -The three frames identified by CA on the Assange corpus

Fig. 8 -

 8 Fig. 8 -CA on DTM aggregated by Origin (terms most contributive to axis 1)

Table 1 -

 1 News agency distribution of dispatches on Julian Assange in 2010-2011 (monthly breaks) The recoding of the Date variable is performed with the menu Manage corpus ➝ Recode time variable . The table itself is available in the Distribution of documents ➝ Two-way table of variables menu.

		Agence France Presse	Associated Press Newswires	Reuters News
	2010-07	1.03	2.40	0.65
	2010-08	4.91	8.00	3.23
	2010-09	1.81	1.60	0.00
	2010-10	3.36	2.40	0.00
	2010-11	6.98	8.80	3.87
	2010-12	45.48	30.40	50.97
	19

Table 2 -

 2 The geography of the Assange corpus (frequencies by country)

		Agence France Presse	Associated Press Newswires	Reuters News
	SE	1.6801	2.1796	1.7514
	GB	1.4403	1.3084	1.6043
	AF	0.3193	0.3443	0.2149
	IQ	0.2735	0.2135	0.2237
	AU	0.7087	0.4511	0.4474
	US	0.4621	0.5647	0.5505
	IS	0.0593	0.0448	0.0353

  hope to have provided with this corpus analysis evidence that opening the black box of proprietary text mining solutions is of major interest for media studies. With R.TeMiS we propose to explore two new dimensions in text mining. The automation of corpus construction and management procedures is first: easily gathering media content with relevant metadata in a reliable way is key to successful text mining in media studies. The extension of the range of statistical tools available through R coding is the second one. Providing the social scientist with standard statistical methods developed in R and facilitating the invention of new tools based on other R packages is also something that media studies -and every other field of the HSS -can benefit from.

	wasn london switzerland	0.5295 0.0563	0.0761 21.0744 36.5385	84.6154	0.5319 0.0381	0.0190	153 18	22	726 52	26	6.7247 -0.0078 1.7676	0.0000 0.4969 0.0386
	stockholm england swiss	0.1384 0.0978	0.3080 24.2424 22.6027	41.5888	0.1209 0.1070	0.1568	39 33	89	165 146	214	6.6734 0.8765 -0.4830	0.0000 0.1904 0.3145
	complaint english germani consensu uk german afghanistan berlin	0.0865 Appendix 3 -Geographical terms within the corpus according to the origin of the 69.4444 0.0264 25 36 6.0599 0.0311 17.6471 0.0374 9 51 -0.4164 0.0326 22.9167 0.0352 11 48 -0.0928 0.1177 57.6271 0.0432 34 59 5.9758 dispatches 0.0138 10.8108 0.0271 4 37 -1.3836 0.0623 31.3433 0.0491 20 67 1.1125 0.2076 21.1268 0.2081 60 284 0.0695 0.0089 14.2857 0.0154 3 21 -0.8422	0.3385 0.4630 0.0000 0.0832 0.1330 0.0000 0.5277 0.1998
	defens afghan franc	0.1384 0.0208	0.1661 31.7460 15.9091	47.5248	0.0923 0.0322	0.0740	39 7	48	126 44	101	5.7897 2.6841 -1.1945	0.0000 0.0036 0.1161
	inc iraq french	0.1696 0.0148	0.0761 21.0300 13.5135	70.9677	0.1707 0.0271	0.0227	49 5	22	233 37	31	5.7751 0.0440 -1.4290	0.0000 0.5175 0.0765
	mayawati iraqi iran	0.0104 0.0593	0.0415 52.6316 100.0000 4.7619	0.0462 0.0278	0.0088	3 19	12	63 38	12	5.6488 -3.4432 3.5393	0.0000 0.0003 0.0002
	women baghdad pakistan seek australia ecuador stem america , , Agence France Presse 0.0346 0.0089 0.0934 0.0208 0.0311 australian 0.3599 spain 0.0059	0.4603 22.2222 10.7143 0.1938 17.6471 29.1667 0.0865 11.6883 15.8537 10.0000	33.4171 42.4242 59.5238	0.0330 0.0205 0.1121 0.0176 0.0564 0.4806 0.0147	0.2916 0.0967 0.0308	9 3 27 6 9 104 2	133 56 25	45 28 153 24 77 656 20	398 132 42	5.6160 0.0262 -1.5640 5.4135 -0.9670 0.3059 5.2540 -1.9882 -3.3951 -1.3050	0.4895 0.0589 0.0000 0.1668 0.3799 0.0000 0.0234 0.0000 0.0003 0.0960
	disclosur american	% 0.2111	0.1592 30.1980 % Le-	44.6602 Global % 0.1480	0.0755	Level 60	46	Global 202	103	5.2407 t value 2.9439	0.0000 Prob. 0.0016
	assault whistleblow organis websit borgstroem rape warrant claim australian dollar organis websit whistleblow bank assault cabl miss arrest defenc Reuters News elmer washington sweden york swedish usa stockholm iceland britain russia british russian london switzerland england swiss english germani uk german afghanistan berlin afghan franc iraq french iraqi iran baghdad pakistan australia ecuador australian spain enrag misconduct america arrest world volunt american word washington , , Reuters News york misconduct miss usa sex femal iceland organ plead russia sweden elmer borgstrom aug author russian swedish tax switzerland stockholm add swiss britain germani british volunt lulzsec german london investig disc berlin england inc assang offens data franc english cutler french uk factbox iran afghanistan pakistan afghan crime updat ecuador iraq theatr spain iraqi	0.2342 0.3127 0.1855 0.7770 0.0514 0.6362 0.4914 0.3763 0.5848 0.0663 0.0000 0.3219 0.0381 0.0485 0.0277 0.3807 0.0035 0.3876 0.0035 % Term/Level % Level/Term 83.9806 90.5882 79.6512 65.3759 100.0000 65.8263 67.3469 69.3267 65.8537 92.4528 0.0000 10.5923 4.3137 5.1282 3.8835 12.1951 1.1236 13.7761 1.1905 0.1453 84.4828 Term/Level 0.2180 22.1831 vel/Term 0.8839 52.4077 0.0900 23.4234 0.6823 49.6552 0.0173 25.0000 0.1218 42.0561 0.0450 18.8406 0.3763 55.0495 0.0346 16.3934 0.3899 56.4706 0.0138 10.5263 0.4968 50.5510 0.0346 19.2308 0.1327 59.3939 0.1073 21.2329 0.0311 45.0980 0.0519 31.2500 0.0203 40.5405 0.0727 31.3433 0.2396 62.3239 0.0415 57.1429 0.0812 47.6190 0.0346 22.7273 0.1909 60.5150 0.0069 5.4054 0.0528 61.9048 0.0208 15.7895 0.0311 51.1111 0.0069 7.1429 0.1272 61.4379 0.0035 4.1667 0.5848 65.8537 0.0104 15.0000 0.0569 95.4545 0.1779 75.0000 0.0541 51.9481 0.7039 63.9606 0.3492 68.2540 0.1630 82.0896 0.1245 45.5446 0.1571 64.6341 0.2139 55.6338 0.0690 45.9459 0.0027 2.5000 0.1541 58.4270 0.0027 10.0000 % % Le-Global % Global % 0.1509 0.1868 0.1260 0.6432 0.0278 0.5231 0.3949 0.2938 0.4806 0.0388 0.1260 0.6432 0.1868 0.2000 0.1509 0.6608 0.0652 0.5956 0.0615 0.0425 0.2081 0.9128 0.0813 0.7436 0.0147 0.1568 0.0505 0.3700 0.0447 0.3736 0.0278 0.5319 0.0381 0.1209 0.1070 0.0374 0.0352 0.0271 0.0491 0.2081 0.0154 0.0923 0.0322 0.1707 0.0271 0.0462 0.0278 0.0330 0.0205 0.1121 0.0176 0.4806 0.0147 0.0322 0.0586 0.0564 0.5956 0.2769 0.0491 0.1480 0.0601 0.2081 0.0813 0.0586 0.0652 0.0147 0.1719 30.7506 0.3026 0.0741 75.7576 0.0242 0.0596 63.7681 0.0505 Term/Level vel/Term 0.0555 23.1638 0.1297 0.0534 90.0000 0.0147 0.0393 47.5410 0.0447 0.8508 23.0337 0.9128 0.0054 6.8966 0.0425 0.0000 0.0000 0.0293 0.0000 0.0000 0.0286 0.4773 37.0968 0.3179 0.0420 81.5789 0.0278 0.8093 26.8966 0.7436 0.0771 70.2703 0.0271 0.0311 44.2308 0.0381 0.1038 16.3551 0.1568 0.0623 77.7778 0.0198 0.1110 56.1644 0.1070 0.3794 25.3465 0.3700 0.0298 45.8333 0.0352 0.4358 28.8235 0.3736 0.0095 10.4478 0.0491 0.0385 100.0000 0.0095 0.0338 37.3134 0.0491 0.6107 28.3747 0.5319 0.2098 37.6214 0.3018 0.0474 88.8889 0.0132 0.0081 28.5714 0.0154 0.0800 16.3636 0.1209 0.0000 0.0000 0.0227 3.1784 49.5568 3.4710 0.0041 7.1429 0.0308 0.1453 50.5155 0.0711 0.0365 61.3636 0.0322 0.0563 37.2549 0.0374 0.0326 100.0000 0.0081 0.0406 81.0811 0.0271 0.0534 48.6486 0.0271 0.0326 100.0000 0.0081 0.0162 31.5789 0.0278 0.1393 16.5493 0.2081 0.0311 82.1429 0.0205 0.0771 20.6349 0.0923 0.1936 39.0710 0.2681 0.0534 75.0000 0.0176 0.0217 66.6667 0.0176 0.1275 18.4549 0.1707 0.0356 92.3077 0.0095 0.0203 75.0000 0.0147 0.0623 33.3333 0.0462	62 653 25 504 4 90 13 277 10 287 4 367 10 97 30 23 14 15 20 176 11 60 9 140 2 38 6 23 2 93 1 431 3 40 92 157 51 2 Level 43 29 287 30 272 23 35 81 127 22 146 25 205 6 27 26 18 29 17 12 47 22 26 15 43 14 20	173 231 137 574 38 470 363 278 432 49 0 93 11 14 8 110 1 112 1 Level 49 42 60 520 258 55 53 2 52 127 25 41 18 4 0 0 161 26 21 7 13 155 16 0 2348 3 49 11 11 143 18 12	206 255 172 878 38 714 539 401 656 53 172 878 255 273 206 902 89 813 84 Global 58 284 1246 111 1015 20 214 69 505 61 510 38 726 52 165 146 51 48 37 67 284 21 126 44 233 37 63 38 45 28 153 24 656 20 44 80 77 813 378 67 202 82 284 111 80 89 20 Global 413 33 69 177 20 61 1246 58 40 39 434 38 1015 37 52 214 27 146 505 48 510 67 13 67 726 412 18 21 165 31 4738 42 97 44 51 11 37 37 11 38 284 28 126 366 24 24 233 13 20 63	Inf Inf 6.9114 6.7515 6.4088 6.3260 6.2132 6.1697 6.0734 5.9654 -8.7000 -8.2627 -7.5871 -7.3793 -7.0169 -6.9955 -5.5320 -5.3834 -5.3295 t value Inf 0.3586 -1.1887 0.4829 -2.8284 0.1979 -3.4695 -0.2999 0.3748 -0.7429 1.0235 -1.4589 -1.8949 -0.1398 1.2844 -0.0627 -1.1518 1.4976 -1.4912 1.8308 2.7341 3.3972 -1.3740 0.1042 1.9018 -2.3708 1.1153 -0.5876 -0.2588 -1.6868 1.7432 -1.9653 6.0734 -0.3531 5.9522 Inf -0.2706 5.6611 5.5504 Inf -2.3723 7.5241 0.4520 -1.6311 -10.0703 6.6564 -3.8939 t value -9.5486 5.9894 1.4945 -8.3283 5.9533 -0.9031 -1.3524 -7.5578 -7.5125 -7.4098 5.6826 3.3620 1.5735 5.6711 -1.2908 -2.8650 5.6035 0.4112 0.2868 -1.0073 2.0776 -7.4073 5.5691 -2.6381 2.2231 -6.6806 5.5153 -2.1394 -2.4858 -6.5328 -6.3881 -6.3297 5.3693 0.8113 1.8577 5.0599 3.2441 2.9925 5.0599 -2.6339 -3.2470 2.8928 -0.9573 -5.7282 4.9778 1.0293 -2.1937 4.8839 1.6737 1.4190	0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Prob. 0.0000 0.3600 0.3146 0.1173 0.4216 0.0023 0.3821 0.0003 0.2288 0.3539 0.0723 0.1530 0.4444 0.0291 0.5250 0.0995 0.0671 0.1247 0.0336 0.0680 0.0003 0.0031 0.4585 0.0847 0.0089 0.0286 0.2784 0.1324 0.0458 0.3979 0.0247 0.0406 0.3620 0.0000 0.0000 0.0000 0.3933 0.0000 0.0000 0.0088 0.0000 0.3257 0.0000 0.0514 0.0000 0.0000 0.0000 Prob. 0.0000 0.0000 0.0675 0.0000 0.0000 0.0881 0.1832 0.0000 0.0000 0.0000 0.0004 0.0578 0.0000 0.0984 0.0021 0.0000 0.3871 0.3405 0.0000 0.1569 0.0189 0.0000 0.0000 0.0042 0.0131 0.0000 0.0000 0.0065 0.0162 0.0000 0.0000 0.0000 0.2086 0.0316 0.0000 0.0006 0.0014 0.0000 0.0006 0.0042 0.0000 0.0019 0.1692 0.0000 0.0000 0.1517 0.0141 0.0000 0.0000 0.0471 0.0780
	Associated Press Newswires % Term/Level % Level/Term bank 0.3083 38.0952 baghdad 0.0356 26.6667 rape 0.2490 11.7647 australia 0.0949 20.9150 whistleblow 0.0385 5.0980 australian 0.3557 18.2927 , , Associated Press Newswires america 0.0830 36.3636 claim 0.1067 8.9776 % % Le-Global % Global % 0.2000 0.0330 0.5231 0.1121 0.1868 0.4806 0.0564 0.2938 american 0.1453 24.2574 0.1480	11 32 120 27 Level 49	Level 104 84 13 36	Global 273 45 714 153 255 656 77 401 Global 202	t value 4.8358 0.1597 -8.6343 -0.9962 -8.3088 -3.8928 2.1677 -8.0251 t value -0.0549	0.4366 0.0000 0.1596 0.0000 0.0000 0.0000 0.0151 Prob. 0.0000 Prob. 0.4781
	organ victim washington	Term/Level 0.1868	0.3253 0.0030 vel/Term 22.1831	53.1073 1.2821	0.2081	0.1297 0.0571	63	94 1	284	177 78	Inf -5.6887 -0.9190	0.0000 0.0000 0.1791
	sex offens spill borgstrom gillard women sweden york swedish usa stockholm iceland britain russia british russian	1.0590 0.1008 0.8237 0.0385 0.3080 0.0356 0.3426 0.0652 0.2596 0.0089	0.5745 0.1177 0.1073 0.1038 0.0000 0.1660 24.5586 30.6306 23.4483 65.0000 41.5888 17.3913 19.6040 36.0656 14.7059 7.8947	40.1937 80.9524 73.8095 75.0000 0.0000 14.0704	0.9128 0.0813 0.7436 0.0147 0.1568 0.0505 0.3700 0.0447 0.3736 0.0278	0.3026 0.0308 0.0308 0.0293 0.0425 0.2916	305 33 237 12 88 12 99 21 75 3	166 34 31 30 0 56	1246 111 1015 20 214 69 505 61 510 38	413 42 42 40 58 398	Inf 8.1259 7.1528 7.1301 -5.2640 -5.1589 2.8629 1.3235 1.7322 3.5957 6.6734 -1.2886 -0.8039 1.8551 -3.6649 -2.4087	0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0928 0.0021 0.0002 0.0416 0.0988 0.0000 0.0318 0.2107 0.0001 0.0080

This is especially true in media studies. As Gerbner once put it, in this field of research, social scientists aim at finding « hidden

Some are distributed for free, especially those developed by small teams of researchers. But such small dedicated tools are usually closed-source and have difficulty competing with commercial solutions since they need to re-implement every feature from scratch despite limited resources.

This feature is based on two state-of-the-art R packages: zoo for time series handling[START_REF] Zeileis | zoo: S3 infrastructure for regular and irregular time series[END_REF] and lattice for plotting[START_REF] Sarkar | Lattice: multivariate data visualization with R: Springerverlag New York[END_REF]. This again means that the generated code can easily be extended for custom representations if need-

ed. 18 According to Shanto Iyengar, episodic frames contrast with thematic frames because they tend to focus the attention on one event (or person) and divert it from a broader context and the public issues underlying the event or person (

[START_REF] Iyengar | Is Anyone Responsible? How Television Frames Political Issues[END_REF].

All terms relating to the agencies' names, reporters' names or bylines, edition process and dates have been removed from the corpus using the 'Manage corpus ➝ Select or exclude terms' menu (the list is the following : "id," "ad," "dk," "gj," "ar," "satter," "raphael," "nn," "edit," "afp," "novemb," "decemb," "rdm," "sr," "ap," "associ," "contribut," "http," "dec," "nov," "aug," "reuter," "feb," "rjm," "nl," "holden," "davi," "shanley," "hosenbal," "bur," "mark," "david," "michael," "adrian," "keith," "mia," "patrick," "stefano," "ambrogi," "ga," "mb," "writer," "press"). Specific terms analysis ('Descriptive analysis of vocabulary ➝ Terms specific of levels... ') relies on the measure of p-values based on an hypergeometric distribution that give the probability of observing such extreme number of occurrences in the level under the independence hypothesis; the sign of tvalues can be used to identify positive (printed first) and negative (printed last) associations.

Referring to documents or cable « caches ».

Referring to Wikileaks as a « secret-spilling website ».

« organ » and « organis » are root stems for terms like « organize » or « organization ». The only difference comes from the fact that they can be written with s (then stemmed as « organis ») or z (then stemmed as « organ »), i.e. using respectively the English or American spellings.

It can be seen in the CA on the unaggregated DTM that the three agencies are located near the center of the first plane (Fig.1) whereas monthly breaks on the Date variable have more scattered coordinates (not shown). This suggests that the corpus is structured by its time rather than source structure.

Conclusion

Due to the peculiarities of news agencies dispatches that obey a very routinized production process and reach a global audience, the coverage of Julian Assange in 2010-2011 is very similar among the three