# Coherent Augmentation of Controversial Comments

Marina Musse, Mathieu Roche, Natalia Grabar

► To cite this version:

Marina Musse, Mathieu Roche, Natalia Grabar. Coherent Augmentation of Controversial Comments. IEEE International Conference on Big Data (IEEE BigData 2025), Hong Mei Peking University; Cheng-Zhong Xu University of Macau; Leong Hou U University of Macau; Xueqi Cheng Chinese Academy of Sciences; Jing Gao Purdue University; Giuseppe Polese University of Salerno, Dec 2025, Macao SAR, China. 10 p. hal-05465253

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# Coherent Augmentation of Controversial Comments

Marina Musse

UMR TETIS

INRAE

Montpellier, France

musse.marina@orange.fr

Mathieu Roche

UMR TETIS

CIRAD

Montpellier, France

mathieu.roche@cirad.fr

Natalia Grabar

UMR 8163 STL

CNRS, Université de Lille

Lille, France

natalia.grabar@univ-lille.fr

**Abstract**—Self-sufficiency has grown in popularity over the past decades as a response to ongoing societal crises. Considering the immediacy and gravity of the challenges at hand, it raises considerable debate within the public sphere, notably on widely used media such as YouTube. We propose to study this phenomenon by performing a sociological analysis of the technical knowledge around self-sufficiency in YouTube comment sections. Such study requires large corpora of annotated data, which are difficult to produce and typically scarce, especially in languages other than English. To overcome the problem, we implement in this work two independent methods to augment a dataset of labeled French-language YouTube comments. A first method, AugArg, relies on the use of argumentative structure as a controversy marker in the original corpus, while the other method, AugLLM, proposes to prompt an LLM to generate controversial comments. The fine-tuning of a CamemBERT model is performed using both the original and augmented data, enabling us to compare the efficiency of both methods in representing controversiality in the context of self-sufficiency. Our results indicate that utilizing argumentative structure in the corpus provides a more controversy-representative set of comments in comparison with LLM-oriented methods.

**Index Terms**—Augmentation, Controversy, YouTube Comments, French, Argumentation, Self-sufficiency.

## I. INTRODUCTION

In the current context of economic, sociological and environmental crises, self-sufficiency, or the capacity of individuals to independently fulfill their needs in food or energy, has shown a strong rise in popularity [1]. However, this also results in disagreements in the public sphere, notably in social media platforms such as Twitter, Reddit and YouTube comment sections [1]. To study the controversial aspect of such phenomena, sociologists and computer scientists lack consequent and annotated corpora, often costly and time-consuming to construct. This brings into focus the augmentation of manually labeled corpora into coherent datasets, adapted for thorougher analysis.

This work proposes to focus on controversy in French-language YouTube comments by implementing two independent augmentation methods: (1) the AugArg method, in which we perform the splitting and recombination of original samples to create new ones, and (2) the AugLLM method, an LLM-oriented augmentation approach. By focusing on French-language data, this paper extends prior work in both controversy identification and textual data augmentation. It also sets itself apart by implementing a method based on a detailed understanding of argumentative structure.

The rest of the paper is structured as follows: we first outline the existing work on *controversy identification* and *textual data augmentation* in Section II. We then describe the dataset employed for this work in Section III. Section IV details the AugArg augmentation method while Section V introduces the AugLLM augmentation method. Finally, we establish a pipeline in order to perform the fine-tuning of CamemBERT for binary controversy classification in Section VI, enabling us to obtain the results discussed in Section VII and outline potential future work orientations.

## II. RELATED WORK

Related work is presented across two lines: controversy identification and textual data augmentation.

### A. Controversy Identification

Several studies have investigated controversy identification on social media. In one work, the researchers look into the argumentative aspect of tweets using features defined as a combination of linguistic, psychometric, n-grams and Twitter-specific markers [2]. With an F1 score of 89.2% obtained with a support-vector machine (SVM), this study showed the model was able to efficiently detect argumentation in tweets. Although this work mainly focused on assessing how claims are supported in discussions on controversial topics, it established clearer overlaps between argumentation mining and controversy identification.

In another work, the classification of tweets as controversial or non-controversial is investigated [3]. Results showed a performance boost in the classification task with the use of language-specific features such as psychological features (e.g. cognition, social words, drives, tone, authenticity) and grammatical features (e.g. POS usage, pronouns, verb tense, comparison, sentiment, subjectivity). These findings help conclude that such features can reliably indicate topic controversiality.

Similarly, an evaluation of controversy prediction on Reddit can be based on textual and structural features [4]. Textual features are classified into four categories: basic features (e.g. n-gram counts), psychometric features (emotion detection), linguistic features (grammar and sentiment), and twitter-specific features (e.g. post length, punctuation, URLs). Feature efficiency was assessed using the accuracy of the classification task, and showed said textual features had greater influence over classification performance than structural features.
Finally, one study addresses the automated analysis of regulatory texts on land artificialization [5]. Its core contribution lies within the identification of textual markers which enable the automatic classification of the data. The authors construct a nomenclature for thematic relevance annotation, relying on adjectives to convey essential information for labeling. This work emphasizes the relevance of textual markers in setting apart crucial information for classification.

## B. Textual Data Augmentation

Numerous approaches have been proposed to address textual data augmentation. Among them, and in relation with our own work, we can cite three experiments. One introduces *instance crossover* and is inspired by genetic algorithms [6]. This method consists in recombining halves of units of text of the same label, in this case Spanish tweets labeled within 4 sentiment categories. The approach proved particularly helpful for minority classes in the context of sentiment analysis classification. It appears to be fairly promising for short-text classification problems.

Another work performs a comprehensive survey of the rising use of LLMs for textual data augmentation [7]. Regarding conditional generation, i.e. the process of generating data with specific constraints, this work mentions the prompting of Transformer Models as an eminent and effective way to generate relevant and coherent data. However, it also highlights limitations such as data contamination, an overuse of synthetic data which can lead to diversity loss and a concern in ethical aspects with the use of potentially sensitive data.

[8] explore a prompt-based approach for data augmentation in the context of hate speech detection on Indonesian Twitter. They used GPT-3 and strategically created prompts containing 10 tweets to generate an 11th one, allowing the model to perform in-context learning and learn the patterns and structure from the provided examples. Results are compared with a back-translation approach on the same dataset, and the authors conclude that, with comparable accuracies obtained on hate speech classification, this approach enables the creation of more distinct and higher-quality samples, making the models less prone to overfitting and offering an interesting lead to further explore in the field of data augmentation.

## III. DATA

In this section, we describe our corpus and its analysis. The data and code for our experiments is publicly available<fnref n="1" />

### A. Corpus selection

The present work utilizes a corpus of YouTube comments obtained from 6 francophone YouTube channels, selected by experts as explicit promoters of self-sufficiency [1] and listed in Figure I. A set of 45,000 comments was initially collected using the official YouTube API, with comments containing emojis, mistypes, grammatical and spelling errors. Notably, only the textual part of the comments was retained and no metadata was associated to it, apart from the channel of origin.

Answers to comments were not taken into account either. A subset of 1,400 comments was manually labeled by two sociologists specialized in agricultural practices according to two dimensions:

1) binary indicator of controversiality: a given comment is considered as either *controversial* (0) or *non-controversial* (1),

2) label falling into one of the 6 following categories: *Diseases and pest management* (P2), *Water management* (P3), *Match between soil quality and cultivated plants needs management* (P6), *Plants and weed management* (P7), *Harvesting* (PRecolte) or *Irrelevant* (nothing).

<table><caption>TABLE I
SOURCE YOUTUBE CHANNELS</caption><thead><tr><td>Channel name</td><td>URL</td></tr></thead><tbody><tr><td>Vivre sans argent</td><td>https://www.youtube.com/@vivresansargent</td></tr><tr><td>La clé des champs</td><td>https://www.youtube.com/@lacledeschamps65</td></tr><tr><td>Le potager d'Olivier</td><td>https://www.youtube.com/@LepotagerdOlivier</td></tr><tr><td>Permaculture agroécologie etc...</td><td>https://www.youtube.com/@permacultureetc</td></tr><tr><td>Le jardin d'Alekil</td><td>https://www.youtube.com/@LejardindAlekil</td></tr><tr><td>L'ArchiPelle</td><td>https://www.youtube.com/@larchipelle</td></tr></tbody></table>

The corpus is stored as a tripartite data structure containing, for each comment, (1) the text of the comment, (2) its controversy label, and (3) its topic label. The corpus is also split into a *training subset*, representing 80% of the comments, and a *testing subset* containing the remaining 20%. This split is performed in a stratified manner regarding the controversy label, meaning the proportion of controversial and non-controversial comments is identical in both subsets.

### B. Corpus analysis

<figure><img src="image_2.png" /><figcaption>Fig. 1. Controversy label distribution of the original corpus</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>Fig. 2. Topic label distribution of the original corpus</figcaption></figure>

1) *Label imbalance:* In this work, we focus on the controversial aspect of comments. However, we make use of the

<footnote n="1">¹https://github.com/marinmss/STAY-controversy.git</footnote>
topical dimension to ensure greater coherence in the augmentation process. We therefore investigated the distribution of both labels. A strong under-representation of topic labels *PRecolte* (1.2%) and *P7* (0.9%) lead to the discard of comments labeled as such, considered to be irrelevant with regards to our perspectives in this work. The remaining comments, retained for this work and referred to by the term *original corpus* in the following sections, are the comments labeled as *P2*, *P3*, *P6* or *nothing*. They sum up to a total of 1,359 comments, of which 1,244 are labeled as non-controversial and 115 as controversial. Among them, a strong imbalance in both controversy and topic labels persists, as can be observed in Figures 1 and 2. Regarding the controversiality of the corpus, we consider this rescaling to have no considerable repercussion, as the ratio remains unchanged (91% to 9%) for the controversy label. Figure 3 illustrates the intersections between both labels (controversy and topic) of the retained comments. We can observe that a vast majority (1,070 out of 1,360 (78%)) of comments are labeled as both non-controversial and irrelevant. We acknowledge that this could potentially influence the efficiency of our augmentation methods.

<figure><img src="image_4.png" /><figcaption>Fig. 3. Cross label analysis of the retained comments</figcaption></figure>

2) Feature analysis: We performed a feature analysis to investigate controversy markers in the original comments. The selected features are (1) number of characters, (2) number of words, (3) POS-tag (part-of-speech tags, which label words with their grammatical role, like noun or adverb) frequencies (*spaCy*), (4) punctuation frequency (*spaCy*), (5) emoji frequency (*emoji*), (6) URL frequency, (7) polarity and subjectivity (*TextBlob*), (8) Named Entity frequency (*spaCy*), (9) French stop-words frequency (*nltk*), (10) number of n-grams. The summary of these features can be observed in Table II for controversial comments and in Table III for non-controversial comments. We observe that in average, controversial comments are longer (420 vs 176 characters) and contain more words (71 vs 31 words) than non-controversial comments. Emojis are more present in non-controversial comments, along with exclamation and interrogation marks. To make these metrics more representative, we performed a normalization by the number of words per comment for the POS-tag metric, and a normalization by the number of character by comment for the emoji and punctuation counts. Regarding

the POS-tag frequency, adverbs are the only POS-tag more present in controversial comments than in non-controversial comments, once normalized by the number of words. Notably, we observed that in both controversy labels, the most present adverbs are predominantly linked to argumentative discourse, with adverbs such as *ne* (*not*), *pas*, and (*thus*) ranking among the 15 most present adverbs in both controversial and non-controversial comments.

<table><caption>TABLE II
FEATURE ANALYSIS OF CONTROVERSIAL COMMENTS (ORIGINAL CORPUS)</caption><thead><tr><th>Feature</th><th>mean</th><th>std</th><th>min</th><th>25%</th><th>50%</th><th>75%</th><th>max</th></tr></thead><tbody><tr><td>nb_char</td><td>420.42</td><td>840.47</td><td>8.00</td><td>113.00</td><td>200.00</td><td>412.00</td><td>7904.00</td></tr><tr><td>nb_words</td><td>71.69</td><td>136.67</td><td>1.00</td><td>19.00</td><td>34.00</td><td>72.50</td><td>1271.00</td></tr><tr><td>ADJ</td><td>5.62</td><td>10.07</td><td>0.00</td><td>1.00</td><td>3.00</td><td>6.50</td><td>92.00</td></tr><tr><td>ADV</td><td>6.37</td><td>11.01</td><td>0.00</td><td>1.00</td><td>3.00</td><td>7.00</td><td>90.00</td></tr><tr><td>NOUN</td><td>16.54</td><td>32.09</td><td>1.00</td><td>4.00</td><td>8.00</td><td>16.00</td><td>292.00</td></tr><tr><td>PUNCT</td><td>9.45</td><td>22.31</td><td>0.00</td><td>1.50</td><td>4.00</td><td>9.00</td><td>218.00</td></tr><tr><td>VERB</td><td>8.75</td><td>16.56</td><td>0.00</td><td>2.00</td><td>4.00</td><td>9.00</td><td>158.00</td></tr><tr><td>nb_emojis</td><td>0.14</td><td>0.37</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>2.00</td></tr><tr><td>nb_URLs</td><td>0.03</td><td>0.16</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>1.00</td></tr><tr><td>polarity</td><td>0.03</td><td>0.24</td><td>-1.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>1.00</td></tr><tr><td>subjectivity</td><td>0.21</td><td>0.36</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.38</td><td>1.00</td></tr><tr><td>nb_stopwords</td><td>35.19</td><td>70.07</td><td>0.00</td><td>9.00</td><td>18.00</td><td>33.00</td><td>654.00</td></tr><tr><td>nb_NER</td><td>1.71</td><td>3.37</td><td>0.00</td><td>0.00</td><td>1.00</td><td>2.00</td><td>31.00</td></tr></tbody></table>

<table><caption>TABLE III
FEATURE ANALYSIS OF NON-CONTROVERSIAL COMMENTS (ORIGINAL CORPUS)</caption><thead><tr><th>Feature</th><th>mean</th><th>std</th><th>min</th><th>25%</th><th>50%</th><th>75%</th><th>max</th></tr></thead><tbody><tr><td>nb_char</td><td>176.84</td><td>230.92</td><td>1.00</td><td>52.00</td><td>108.50</td><td>210.50</td><td>2945.00</td></tr><tr><td>nb_words</td><td>31.28</td><td>40.66</td><td>1.00</td><td>9.00</td><td>20.00</td><td>37.00</td><td>534.00</td></tr><tr><td>ADJ</td><td>2.49</td><td>3.45</td><td>0.00</td><td>1.00</td><td>1.00</td><td>3.00</td><td>39.00</td></tr><tr><td>ADV</td><td>2.56</td><td>3.93</td><td>0.00</td><td>0.00</td><td>1.00</td><td>3.00</td><td>49.00</td></tr><tr><td>NOUN</td><td>7.33</td><td>9.28</td><td>0.00</td><td>2.00</td><td>5.00</td><td>9.00</td><td>123.00</td></tr><tr><td>PUNCT</td><td>3.84</td><td>5.45</td><td>0.00</td><td>1.00</td><td>2.00</td><td>5.00</td><td>86.00</td></tr><tr><td>VERB</td><td>3.83</td><td>5.54</td><td>0.00</td><td>1.00</td><td>2.00</td><td>5.00</td><td>68.00</td></tr><tr><td>nb_emojis</td><td>0.67</td><td>2.46</td><td>0.00</td><td>0.00</td><td>0.00</td><td>1.00</td><td>52.00</td></tr><tr><td>nb_URLs</td><td>0.01</td><td>0.15</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>2.00</td></tr><tr><td>polarity</td><td>0.07</td><td>0.22</td><td>-1.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>1.00</td></tr><tr><td>subjectivity</td><td>0.19</td><td>0.34</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.09</td><td>1.00</td></tr><tr><td>nb_stopwords</td><td>14.49</td><td>20.47</td><td>0.00</td><td>3.00</td><td>8.00</td><td>17.00</td><td>276.00</td></tr><tr><td>nb_NER</td><td>1.37</td><td>1.59</td><td>0.00</td><td>0.00</td><td>1.00</td><td>2.00</td><td>16.00</td></tr></tbody></table>

Following this analysis, our purpose is twofold: (1) augment the analyzed data, and (2) automatically predict the controversial nature of comments. In Sections IV and V, we describe the AugArg and AugLLM approaches for the data augmentation process, while Section VI presents the results of the categorization of comments as either controversial or not, both on original and augmented corpora.

## IV. AUGMENTATION WITH AUGARG

The AugArg method was conceptualized as a way to augment the corpus by pairing the cross-over augmentation method with specificities linked to controversy in the data. Indeed, the high presence of adverbs in YouTube comments has prompted the hypothesis that such markers can be utilized to segment the data into meaningful portions. Therefore, this method consists in splitting real comments into elements and then recombining them, according to 5 steps, which we describe below: (1) the construction of an adverbial resource, (2) the identification of Argumentative Components (AC), (3) the recombination of the identified elements, (4) the automatic labeling of the obtained augmented samples, (5) a description of the obtained dataset.
## A. Construction of an Adverbial Resource

A resource with argumentative adverbs linked to argumentation was constructed to allow for the method to be put into place. The basis for this resource is an existing work from the Université de Louvain<fnref n="2" />, a thorough set of 198 French-language adverbs organized into 17 function categories such as alternative (ou (or)), cause (car, puisque (because)), and comparison (ainsi que (as well as)), with some adverbs appearing in multiple categories at a time. It is for instance the case of the adverb donc (hence), which belongs to both consequence and conclusion. This set was filtered in two ways: taking into account their (1) semantics and (2) distribution.

1) **Filtering of Adverbs by Semantics:** Only adverbs that may lead to binary splitting are kept. Other adverbs are discarded, such as tantôt ... tantôt (either ... or) and 7 other adverbs, which split the text into three components or more. Furthermore, the adverb comme (like), which was considered to be overly ambiguous in the French language, was removed from the selection. No other adverb was discarded for its versatility, albeit some belonged to multiple categories. At this step, a total of 189 adverbs are kept into consideration.

2) **Filtering of Adverbs by Distribution:** Another issue is related to the length differences between the snippet of text preceding an adverb and the snippet comming after it. Indeed, it is a possibility that an argumentative adverb appears as the first or second word in a comment, making the very short text preceding it less susceptible of carrying meaningful information. To address this, the position of each adverb was computed across all comments of the corpus, normalized by the length of each comment, as well as statistically analyzed. More specifically, we investigated the *normalized position* of each adverb in each comment, i.e. the word index of the adverb divided by the number of words of the comment it occurred in. Overall, the number of occurrences of each adverb was computed across all comments, as well as the mean normalized position, its standard deviation, median, and first and third quartiles. This allowed the setting of a bipartite condition, and therefore a more thorough selection of elements prone to splitting text into balanced components. Adverbs of which (1) the mean normalized position fell within their own inter-quartile range and (2) the standard deviation was lower than the third quartile were retained. This preprocessing stage excluded 155 more adverbs, keeping a total of 52 adverbs in our adverbial resource. For the benefit of subsequent work, the obtained dictionary was flattened, i.e. the adverbs were set as entry keys and the list of corresponding categories set as values. In this final resource with 52 adverbs, each adverb is assigned a list containing all categories it belongs to.

## B. Identification of argumentative components

Each comment containing an adverb from the resource is split into 3 parts: (1) the snippet of text appearing before the detected adverb, (2) the detected adverb, (3) the snippet of text

appearing after the adverb. In this context, the term argumentative component or AC refers to the snippets appearing either before or after the detected adverb. More specifically, we call ACbefore the AC appearing before the adverb, and ACafter the snippet appearing after the adverb for a given comment. These decomposition and nomenclature are illustrated in Figure 4.

<figure><img src="image_5.png" /><figcaption>Fig. 4. Illustration of a comment split by the adverb because.</figcaption></figure>

Following this, we obtain a structure in which we store, for each comment containing an adverb from the resource: (1) the comment of origin, (2) its controversy label, (3) its topic label, (4) its ACbefore, (5) its adverb, (6) its adverb categories according to the adverbial resource, (7) its ACafter. We consider ACs to be carrying both the controversy and topic label of their comment of origin. For instance, a controversial comment labeled nothing results in a controversial nothing ACbefore and a controversial nothing ACafter.

## C. Recombination of Argumentative Components

1) **Argumentative equivalence:** In the recombination step, we search for pairs of comments which can be recombined into a new comment. To do so, a concept of argumentative equivalence is defined to describe compatible comments. We consider two comments to be equivalent if they: (1) carry the same topic label, and (2) both contain an adverb belonging to a common category according to the adverbial resource (cause, for instance). As an illustration, on the left-hand side of Figure 5, we can observe two original comments which both contain an adverb of the cause category, respectively because and since, and which are both carrying the nothing topic label. Therefore, these two comments display argumentative equivalence and are compatible for recombination.

2) **Recombination principle:** Once equivalence is established between two comments, we proceed to the recombination of their ACs to create new comments. From one pair of compatible comments (c1,c2), we produce a pair of new comments (c3,c4) which are defined as:

$$c3 = AC_{before}_{c1} + adverb + AC_{after}_{c2}$$

$$c4 = AC_{before}_{c2} + adverb + AC_{after}_{c1}$$

For each new comment, the utilized adverb is randomly drawn from the common adverb category. This is illustrated in Figure 5, where the two new comments on the right are constructed with a randomly drawn adverb from the cause category, common to the original comments.

We have elected to allow multiple category affiliation per adverb. With respect to this, when the intersection of the categories of two adverbs contains multiple elements, all are considered equally and iteratively. This was done to avoid potential misclassifications, acknowledging the incoherencies it might introduce. We consider these less semantically altering than a single category assignation per adverb, which could lead to incompatibility during the recombination step.

<footnote n="2">²https://sites.uclouvain.be/infosphere_boreal/fichiers_communs/module7/connecteurs.html</footnote>
<figure><img src="image_6.png" /><figcaption>Fig. 5. Illustration of the recombination step</figcaption></figure>

3) Interchangeable adverbial categories: The AugArg method also questions the importance of AC ordering during recombination. Indeed, we consider some adverbial categories to permit the mutual substitution of *ACbefore* and *ACafter*, while preserving identical or closely equivalent meaning. As an illustration, the two recombinations

(1) *I dislike this video as much as I hate the channel.*

(2) *I hate the channel as much as I dislike this video.*

are considered in this approach to carry the same meaning. Out of the 17 considered categories, 5 were considered as interchangeable: *alternative (ou (or)), comparison (ainsi que (as well as), plus que (more than), ...), classification (après (after), de plus (moreover),...), explication (soit (either), ...), illustration (ainsi (thus), en particulier (in particular)).* Hence, any adverb belonging to one of these 5 categories splits a comment into interchangeable ACs, meaning a pair of such compatible comments (c1,c2) produces pair of new comments (c3,c4) following the recombination principle defined in Section IV-C2, and an additional pair (c5,c6), defined as follows:

$$c5 = ACafter_{c1} + adverb + ACbefore_{c2}$$

$$c6 = ACafter_{c2} + adverb + ACbefore_{c1}$$

In comments c5 and c6, the *ACbefore* of both original comments are placed before the adverb, while the *ACafter* are placed after it. In Figure 6, we can see on the left-hand side a pair of compatible comments with adverbs of the *nothing* topic label and the *comparison* category, which we listed as interchangeable. These original comments are recombined into 4 new comments, of which c5 and c6, boxed in purple, are obtained by positioning *ACafter* of c1 and c2 before the new adverbs and their *ACbefore* after it.

## D. Automatic labeling of new samples

The adopted labeling strategy was primarily motivated by the necessity to mitigate corpus imbalance through the generation of controversial comments. It was assumed controversy behaves as a monotonic property, meaning that once controversy is introduced, adding neutral or non-controversial content does not negate its effect. This is supported by prior work such as [9] and [10]. We thus argue that a comment can be considered as controversial if at least one of its ACs is controversial.

<figure><img src="image_7.png" /><figcaption>Fig. 6. Illustration of the recombination step for interchangeable categories</figcaption></figure>

We remind that the concept of *argumentative equivalence* did not restrict the recombination of ACs of different controversy labels, meaning some controversial *ACbefore* were deliberately associated to non-controversial *ACafter* and vice-versa. This resulted in 4 cases of new comments, described as follows:

* The CCC case relies on the assumption that two controversial ACs combine into a new controversial comment.

* The CNC and NCC cases both combine one controversial AC and one non-controversial AC, in different orders, and result in a controversial comment.

* In case NNN, we confidently assume that combining components obtained from two non-controversial comments will result in a non-controversial comment.

These cases can also be observed in Table IV, where $AC_1$ and $AC_2$ respectively refer to the first and second occurring AC in the new comment. We remind that in the case of interchangeable categories, both $AC_1$ and $AC_2$ can be either an *ACbefore* or an *ACafter* in their respective comment of origin. At this stage, we emit the hypothesis that cases CCC and NNN will result in strong representations of respectively the non-controversial and the controversial labels. On the other hand, we assume cases CNC and NCC are at higher risk of creating non-controversial text. While the assumption of monotonic controversy seems well-founded, it remains a heuristic prone to exceptions. We acknowledge a risk of two innocuous AC being recombined and thus canceling each other out, particularly in the case of interchangeable categories. Moreover, this strategy raises compelling interrogations on which ACs (*ACbefore* or *ACafter*) carry controversy and whether the controversy from *ACbefore* may be resolved by *ACafter* in some cases.

## E. Description of the obtained dataset

1) Label imbalance: The obtained dataset, referred to as the AugArg dataset, was obtained by applying the AugArg method to the training subset (Section III). It sums up to a total of 67,570 new comments, of which 56,910 (84.2%) are labeled as
<table><caption>TABLE IV
CASES OF CONTROVERSY INHERITANCE IN NEW COMMENTS, AND THEIR
LABELING</caption><thead><tr><th>Case</th><th>AC<sub>1</sub> label</th><th>AC<sub>2</sub> label</th><th>New comment label</th></tr></thead><tbody><tr><td>CCC</td><td>Controversial</td><td>Controversial</td><td>Controversial</td></tr><tr><td>CNC</td><td>Controversial</td><td>Non-Controversial</td><td>Controversial</td></tr><tr><td>NCC</td><td>Non-Controversial</td><td>Controversial</td><td>Controversial</td></tr><tr><td>NNN</td><td>Non-Controversial</td><td>Non-Controversial</td><td>Non-Controversial</td></tr></tbody></table>

non-controversial, and 10,660 (15.8%) as controversial. These proportions indicate the imbalance of the controversy label was slightly corrected, but remains strongly present.

2) Semantic coherence: To assess semantic coherence, a random sample of 100 augmented comments from the AugArg dataset was manually inspected. A human annotator checked for the fulfillment of the following four conditions in each comment: (1) the comment does not introduce any contradictory ideas, (2) it does not contain any abrupt change in topics, (3) it demonstrates coreference consistency i.e. pronouns are coherent throughout the text, (4) it does not contain any syntactic disfluency. For each fulfilled condition, the inspected comment is assigned a point, resulting in a score on scale from 0 (low semantic coherence) to 4 (high semantic coherence).

Across these 100 samples, the obtained average score is of 3.25 out of 4, and a majority of comments (54%) obtained a perfect score (4/4). Coherent coreference (92%) and contradiction (92%) showed to be the strongest dimensions, while topic change (66%) and syntactic fluidity (72%) displayed relatively weaker success rates. Overall, these results provide a clear indicative picture of a strong semantic coherence in the augmented dataset.

## V. AUGMENTATION WITH AUGLLM

A second method exploited for the textual data augmentation is based upon LLM generation. This method consists in passing a textual description of a task to an LLM. In this context, the task is the generation of controversial, French-language YouTube comments on the topic of self-sufficiency. Prior work has shown LLMs have become increasingly utilized in recent augmentation work, in which they are often praised for providing qualitative and highly-varied data (Section II). Through this method, we aim to reflect on the capacity of an LLM to accurately depict controversy in French-language YouTube comments.

### A. LLM selection

A wide range of LLMs offer the possibility of performing LLM-generation through publicly available Application Programming Interfaces (API). In this work, Mistral<fnref n="3" /> was selected due to its demonstrated strong performance in European languages across a variety of generative tasks [11].

### B. Prompt development

The construction of the prompt was guided by the objective of obtaining comments that resemble real comments from our

corpus. Indeed, the prompt needed to result in the generation of YouTube French-language comments which display controversiality on the topic of self-sufficiency. Following an attempt at both zero-shot and few-shots approaches, zero-shot was retained, seeming to better avoid model bias and potential pattern repetitions. Furthermore, given Mistral's strong French-language generative capabilities, zero-shot prompting appeared to be sufficient for this task. This implies no sample of the original corpus was provided to the model. The process of obtaining a concise instruction, clearly specifying the task for the model, comprised many iterative refinements. Notably, it was observed that batches of 5 comments were most efficient in this context, an observation later confirmed by a literature review on the subject [12]. It was also noticed that, when prompted to generate comments on self-sufficiency, the model tends to include the term in all generated comments. This lead to the retention of the final prompt in Figure 7, which queries for batches of 5 comments that do not contain the term self-sufficiency.

> """Genère exactement 5 commentaires YouTube controversés sur une vidéo en français portant sur l'auto-suffisance, sans utiliser le mot 'auto-suffisance'. Retourne les commentaires sous forme d'une liste numérotée, un par ligne, comme ceci: 1. ...\n 2. ...\n 3. ...\n 4. ...\n 5. ...\n"""

Translation: Generate exactly 5 controversial YouTube comments on a video in French about self-sufficiency, without using the word 'self-sufficiency'. Return the comments as a numbered list, one per line, like this [...]

Fig. 7. Retained prompt

### C. Implementation

The Ollama API was used for this work, as it gives access to a wide variety of pretrained Mistral models with no restrictions on the number of queries. This was a considerable advantage given the objective of generating a large amount of comments in French. More specifically, Mistral 7b was selected, providing an optimal balance between resource requirement and performance. In total, the prompting was queried in 260 batches of 5 comments, resulting in a dataset of 1,300 elements.

### D. Description of the obtained dataset

Out of the 1,300 obtained comments, referred to as the AugLLM corpus, 100 were manually analysed in a first step. Although the model was instructed to generate controversial comments, the results seemed to show it was unable to consistently produce controversiality in text. Indeed, as illustrated by the example in Figure 8, a majority of generated comments do not appear to carry controversy, or at least would not be considered as controversial by a human observer.

## VI. CONTROVERSY CLASSIFICATION

The task of automatic controversy classification is addressed with two main objectives in mind. Firstly, it shows the capacity of models to classify controversial comments and separate

<footnote n="3">³https://docs.mistral.ai/</footnote>
C'est fantastique que vous ayez choisi ce sujet. L'auto-suffisance est une chose importante à prendre en considération dans notre société actuelle. J'ai beaucoup appris de cette vidéo, merci !

Translation: It's fantastic that you chose this topic. Self-sufficiency is an important thing to consider in our current society. I learned a lot from this video, thank you!

<caption>Fig. 8. Example of a comment generated with AugLLM</caption>

them from non-controversial comments. Secondly, it assesses the impact of the augmented data on the results. It was deemed necessary to establish an evaluation pipeline in order to effectively compare the quality of the original corpus and the augmented corpora. This evaluation pipeline aims to represent an immutable protocol which can be applied to different configurations and shows the evolution of the results. It takes as an input a data configuration object, composed of a training dataset and a testing dataset. It then performs the fine-tuning of a CamembertForSequenceClassification model<fnref n="4" /> on the training dataset using a Trainer object from the Transformers library. It also assesses the performances of the obtained fine-tuned model on the testing dataset from the configuration object.

The fine-tuning process is performed with the same hyperparameters and the same cross-validation settings independently of the data configuration object passed as a parameter. At each run, the training dataset is split into 5 folds, the training is done in 10 epochs with a batch size of 16 and an initial learning rate is of 2e-5, backed up by 500 warmup steps. These hyperparameters settings were selected through a manual observation of the loss function dynamics in first attempts. The said observations, combined with a close reading of the Trainer documentation<fnref n="5" />, also lead to the setting the training argument load_model_at_end to True, and the setting of the parameter metric_for_best_model to accuracy. This implies that the obtained fine-tuned model for each fold is the model with the best evaluation accuracy across all 10 epochs. Indeed, the obtained plots indicated the evaluation accuracy was generally the highest right before the model started overfitting on the original corpus. This confirmed the choice of 10 epochs to be relevant, as our analysis indicated that the model consistently reached its best performance between the 4th and the 8th epoch according to the chosen metric. Furthermore, for consistency purposes, the evaluation accuracy metric was also used to define the best performing model over all folds. In conclusion, the model retained as the final fine-tuned model is the best performing out of the 5 models obtained across all 5 folds.

# VII. RESULTS

## A. Original corpus: baseline definition

In order to establish a baseline for controversy classification, this pipeline was applied to the testing subset of the original

data, defined in Section III and previously used to obtain the augmented AugArg dataset. Table V shows the metrics obtained across all 5 folds of the training phase, and indicates Fold 2 is the best performing. With an accuracy surpassing 91% in all folds, the model performs strong generalization on this classification task. However, a more thorough examination raises attention on the differences in performance between the two label classes in the data: the precision, recall and F1-score are considerably lower for the controversial class than for the non-controversial one. This thus emphasizes the need to augment the data in order to obtain a more balanced dataset.

<table><caption>TABLE V
BASELINE MODEL PERFORMANCE ON EACH CROSS-VALIDATION FOLD.
FOLD 2 IS THE BEST PERFORMING FOLD.</caption><thead><tr><th></th><th>Precision</th><th>Recall</th><th>F1-score</th></tr></thead><tbody><tr><td><strong>Fold 1</strong></td><td></td><td></td><td><strong>Accuracy: 0.9154</strong></td></tr><tr><td>Non-Controversial</td><td>0.9154</td><td>1.0000</td><td>0.9559</td></tr><tr><td>Controversial</td><td>0.0000</td><td>0.0000</td><td>0.0000</td></tr><tr><td>Macro Avg</td><td>0.4577</td><td>0.5000</td><td>0.4779</td></tr><tr><td>Weighted Avg</td><td>0.8380</td><td>0.9154</td><td>0.8750</td></tr><tr><td><strong>Fold 2 (Best Fold)</strong></td><td></td><td></td><td><strong>Accuracy: 0.9338</strong></td></tr><tr><td>Non-Controversial</td><td>0.9602</td><td>0.9679</td><td>0.9640</td></tr><tr><td>Controversial</td><td>0.6190</td><td>0.5652</td><td>0.5909</td></tr><tr><td>Macro Avg</td><td>0.7896</td><td>0.7665</td><td>0.7775</td></tr><tr><td>Weighted Avg</td><td>0.9313</td><td>0.9338</td><td>0.9325</td></tr><tr><td><strong>Fold 3</strong></td><td></td><td></td><td><strong>Accuracy: 0.9191</strong></td></tr><tr><td>Non-Controversial</td><td>0.9219</td><td>0.9960</td><td>0.9575</td></tr><tr><td>Controversial</td><td>0.6667</td><td>0.0870</td><td>0.1538</td></tr><tr><td>Macro Avg</td><td>0.7943</td><td>0.5415</td><td>0.5557</td></tr><tr><td>Weighted Avg</td><td>0.9003</td><td>0.9191</td><td>0.8896</td></tr><tr><td><strong>Fold 4</strong></td><td></td><td></td><td><strong>Accuracy: 0.9228</strong></td></tr><tr><td>Non-Controversial</td><td>0.9318</td><td>0.9880</td><td>0.9591</td></tr><tr><td>Controversial</td><td>0.6250</td><td>0.2174</td><td>0.3226</td></tr><tr><td>Macro Avg</td><td>0.7784</td><td>0.6027</td><td>0.6408</td></tr><tr><td>Weighted Avg</td><td>0.9059</td><td>0.9228</td><td>0.9052</td></tr><tr><td><strong>Fold 5</strong></td><td></td><td></td><td><strong>Accuracy: 0.9301</strong></td></tr><tr><td>Non-Controversial</td><td>0.9562</td><td>0.9677</td><td>0.9619</td></tr><tr><td>Controversial</td><td>0.6190</td><td>0.5417</td><td>0.5778</td></tr><tr><td>Macro Avg</td><td>0.7876</td><td>0.7547</td><td>0.7699</td></tr><tr><td>Weighted Avg</td><td>0.9264</td><td>0.9301</td><td>0.9280</td></tr></tbody></table>

In order to obtain a broader picture, a Baseline test was also performed on the entirety of the original corpus. Applied to the 1,359 comments, the fine-tuned model achieved high values with precision, recall and F1-score all exceeding 86% for both classes, and an overall accuracy of approximately 98% (Table VI). This confirms that the fine-tuned model has efficiently learned the task of controversy classification in YouTube comments. However, it is important to note that these particular performance results should be handled with precaution considering 80% of the passed dataset was used for the training of the model.

## B. Results for the AugArg Dataset

1) Fine-tuning Camembert: The fine-tuning pipeline described above was utilized to assess the quality of the AugArg dataset for the task of controversy classification. The hyperparameters are identical to the ones employed for Baseline definition. The fine-tuned model's performance is evaluated on the test split (20%) of the original corpus.

<footnote n="4">⁴https://huggingface.co/docs/transformers/model_doc/camembert</footnote>

<footnote n="5">⁵https://huggingface.co/docs/transformers/main_classes/trainer</footnote>
<table><caption>TABLE VI
BASELINE MODEL PERFORMANCE ON THE ORIGINAL CORPUS</caption><thead><tr><th></th><th>Precision</th><th>Recall</th><th>F1-score</th></tr></thead><tbody><tr><td><strong>Class-level</strong></td><td></td><td></td><td></td></tr><tr><td>Non-controversial</td><td>0.9872</td><td>0.9936</td><td>0.9904</td></tr><tr><td>Controversial</td><td>0.9259</td><td>0.8621</td><td>0.8929</td></tr><tr><td><strong>Whole corpus</strong></td><td></td><td></td><td><strong>Accuracy: 0.9824</strong></td></tr><tr><td>Macro Avg</td><td>0.9566</td><td>0.9278</td><td>0.9416</td></tr><tr><td>Weighted Avg</td><td>0.9820</td><td>0.9824</td><td>0.9821</td></tr></tbody></table>

<table><caption>TABLE VII
VALIDATION METRICS OBTAINED DURING THE FINE-TUNING OF
CAMEMBERT ON THE AUGARG DATASET (FOLD 1)</caption><thead><tr><th></th><th>Precision</th><th>Recall</th><th>F1-score</th></tr></thead><tbody><tr><td><strong>Class-level</strong></td><td></td><td></td><td></td></tr><tr><td>Non-controversial</td><td>1.0000</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Controversial</td><td>1.0000</td><td>1.0000</td><td>1.0000</td></tr><tr><td><strong>Whole corpus</strong></td><td></td><td></td><td><strong>Accuracy: 1.0000</strong></td></tr><tr><td>Macro Avg</td><td>1.0000</td><td>1.0000</td><td>1.0000</td></tr><tr><td>Weighted Avg</td><td>1.0000</td><td>1.0000</td><td>1.0000</td></tr></tbody></table>

The metrics in Table VII were obtained during the training phase of the Fold 1, identified as the best fold. Accuracy, precision, recall and F1-score reach perfect scores immediately, and stay above 99% across all folds. The loss plots discarded overfitting as both the training and evaluation losses lean towards a null value from early training steps (Figure 9). However, this raises questions on the quality of the obtained model, inquiring the need to evaluate its generalization capacities. It may suggest that the dataset is relatively simple or that the model has memorized patterns specific to the AugArg dataset rather than generalizable linguistic cues. Possible factors include data leakage or insufficient data diversity. The nature of the AugArg method involves repetition of snippets of text as a single AC is used in several recombinations, which also may explain overly high performances. To assess the extent of this possibility, the AugArg model performance was evaluated on the testing subset of the initial corpus as well.

The fine-tuned model obtained through this process, also referred to as the AugArg model, was thus tested on the original corpus. The obtained metrics are available in Table

VIII and show that the model classifies comments rather accurately with an overall accuracy of over 89%, but is biased towards the non-controversial class with a strong imbalance in precision, recall and F1-score values. This bias strongly echoes with the metrics obtained on our baseline model.

<table><caption>TABLE VIII
AUGARG MODEL PERFORMANCE ON THE ORIGINAL CORPUS (TESTING SUBSET)</caption><thead><tr><th></th><th>Precision</th><th>Recall</th><th>F1-score</th></tr></thead><tbody><tr><td><strong>Class-level</strong></td><td></td><td></td><td></td></tr><tr><td>Non-controversial</td><td>0.9368</td><td>0.9518</td><td>0.9442</td></tr><tr><td>Controversial</td><td>0.3684</td><td>0.3043</td><td>0.3333</td></tr><tr><td><strong>Whole corpus</strong></td><td></td><td></td><td><strong>Accuracy: 0.8971</strong></td></tr><tr><td>Macro Avg</td><td>0.6526</td><td>0.6281</td><td>0.6388</td></tr><tr><td>Weighted Avg</td><td>0.8887</td><td>0.8971</td><td>0.8926</td></tr></tbody></table>

2) *Baseline test*: To further investigate the quality of the AugArg dataset, it was passed as an argument to the Baseline model for controversy classification. The metrics in Table IX show that this new dataset still results in a slight bias of the model towards the more represented non-controversial class, although, precision is only around 2% lower in the controversial class. To investigate the validity of our labeling inheritance process, we computed a separate classification report for four cases (CCC, CNC, NCC, NNN), as indicated in Table IX:

* The NNN hypothesis obtained an accuracy of over 99%, closely followed by CCC with 97% accuracy.

* The CNC and NCC hypotheses, although both show very high precision values, proved to be less efficient as they show lower accuracy.

This confirms our hypothesis that AugArg leads to higher performances when applied within controversy labels (cases CCC and NNN), but it does not discard the recombination of controversial and non-controversial elements. This taken into consideration, the model reaches an accuracy of 95% on the whole AugArg dataset, allowing us to conclude that it accurately represents controversy according to the Baseline model.

Table X displays the percentage improvement between the evaluation metrics obtained by the Baseline on its best fold, Fold 2, and the Baseline performances on the AugArg dataset. We observe that precision has decreased by 0.83% for non-controversial comments; however, all other metrics show improvement with the AugArg dataset, including an increase in accuracy of 1.74%. For the controversial class, the results show a considerable improvement, with a rise of above 25% in all three considered metrics, reaching up to 50% in the case of precision.

### C. Results for the AugLLM dataset

In a similar manner, to further investigate the quality of the AugLLM dataset, we computed the predictions of each generated comment according to the Baseline model for controversy classification. This showed that 71.4% of

<figure><img src="image_8.png" /><figcaption>Fig. 9. Loss plot obtained during the fine-tuning of Camembert on the AugArg dataset (Fold 1)</figcaption></figure>
<table><caption>TABLE IX
BASELINE MODEL PERFORMANCE FOR THE CLASSIFICATION OF THE AUGARG CORPUS, WITH AUTOMATIC LABELING CASES COMPARISON</caption><thead><tr><th></th><th>Precision</th><th>Recall</th><th>F1-score</th></tr></thead><tbody><tr><td><strong>Class-level</strong></td><td></td><td></td><td></td></tr><tr><td>Non-controversial</td><td>0.9522</td><td>0.9911</td><td>0.9713</td></tr><tr><td>Controversial</td><td>0.9340</td><td>0.7164</td><td>0.8109</td></tr><tr><td><strong>Whole dataset</strong></td><td></td><td></td><td><strong>Accuracy: 0.9501</strong></td></tr><tr><td>Macro Avg</td><td>0.9431</td><td>0.8538</td><td>0.8911</td></tr><tr><td>Weighted Avg</td><td>0.9495</td><td>0.9501</td><td>0.9474</td></tr><tr><td><strong>CCC</strong></td><td></td><td></td><td><strong>Accuracy: 0.9735</strong></td></tr><tr><td>Macro Avg</td><td>0.5000</td><td>0.4868</td><td>0.4933</td></tr><tr><td>Weighted Avg</td><td>1.0000</td><td>0.9735</td><td>0.9866</td></tr><tr><td><strong>CNC</strong></td><td></td><td></td><td><strong>Accuracy: 0.6482</strong></td></tr><tr><td>Macro Avg</td><td>0.5000</td><td>0.3241</td><td>0.3933</td></tr><tr><td>Weighted Avg</td><td>1.0000</td><td>0.6482</td><td>0.7866</td></tr><tr><td><strong>NCC</strong></td><td></td><td></td><td><strong>Accuracy: 0.7614</strong></td></tr><tr><td>Macro Avg</td><td>0.5000</td><td>0.3807</td><td>0.4323</td></tr><tr><td>Weighted Avg</td><td>1.0000</td><td>0.7614</td><td>0.8646</td></tr><tr><td><strong>NNN</strong></td><td></td><td></td><td><strong>Accuracy: 0.9911</strong></td></tr><tr><td>Macro Avg</td><td>0.5000</td><td>0.4956</td><td>0.4978</td></tr><tr><td>Weighted Avg</td><td>1.0000</td><td>0.9911</td><td>0.9955</td></tr></tbody></table>

<table><caption>TABLE X
COMPARISON OF BASELINE MODEL PERFORMANCE ON THE ORIGINAL CORPUS TESTING SUBSET (FOLD 2 - BEST) AND BASELINE MODEL PERFORMANCE ON THE AUGARG DATASET. PERCENTAGE IMPROVEMENTS ARE COMPUTED FROM TESTING SUBSET TO AUGARG SUBSET</caption><thead><tr><th></th><th>Testing subset</th><th>AugArg dataset</th><th>Improvement (%)</th></tr></thead><tbody><tr><td><strong>Whole corpus</strong></td><td></td><td></td><td></td></tr><tr><td>Accuracy</td><td>0.9338</td><td>0.9501</td><td>+1.74</td></tr><tr><td><strong>Non-controversial</strong></td><td></td><td></td><td></td></tr><tr><td>Precision</td><td>0.9602</td><td>0.9522</td><td>-0.83</td></tr><tr><td>Recall</td><td>0.9679</td><td>0.9911</td><td>+2.38</td></tr><tr><td>F1-score</td><td>0.9640</td><td>0.9713</td><td>+0.76</td></tr><tr><td><strong>Controversial</strong></td><td></td><td></td><td></td></tr><tr><td>Precision</td><td>0.6190</td><td>0.9340</td><td>+50.7</td></tr><tr><td>Recall</td><td>0.5652</td><td>0.7164</td><td>+26.8</td></tr><tr><td>F1-score</td><td>0.5909</td><td>0.8109</td><td>+37.2</td></tr></tbody></table>

the comments generated by Mistral are classified as *non-controversial* by the Baseline, albeit the prompt requested for controversial comments only.

## D. Discussion

The AugArg method relies on argumentative structure detection within the comments, and multiplies by almost 50 the number of elements in the dataset. It does not introduce new data and solely relies on the recombination of existing portions of text from the original corpus. While it highlights and maintains the imbalance in classes of the dataset in its label proportions, it still successfully reduces this imbalance from a 91-9% ratio to a 71-29% ratio. The performances obtained with the Baseline model for the classification task showed that this augmentation method effectively reduces the bias of the model towards the non-controversial class, and that the obtained corpus accurately represent controversy in this context. However, the AugArg model, obtained by fine-tuning Camembert on the AugArg dataset, showed to be highly biased towards the non-controversial comments albeit an encouraging accuracy of 89% when tested on the original corpus.

The AugLLM method relies on the prompting of the LLM Mistral to create new samples of comments with limited contextualization. Indeed, the utilized prompt only inquired for controversial comments, in French, and on the topic of self-sufficiency, with no examples from the original corpus provided. This method produced a dataset similar in size to the original corpus, but it poorly represented controversiality. In fact, a majority of the generated comments was classified as non-controversial by our Baseline model, and deemed non-controversial by our manual analysis. From these results, we conclude that LLM-generation of French controversial YouTube comments using Mistral, a high-performing model in generative tasks, produces data with too few controversiality markers for efficient model generalization. Overall, this brings interesting insights on the efficiency of LLM-prompting to create controversial content. In the literature review in Section II, we established a strong link between controversy and argumentation, and we confirmed controversy detection and generation involve subtle cognitive and social cues. Large language models, although very efficient in a vast range of generative tasks, lack a general understanding of human mechanisms involved in meaning, understandings and social interactions [13]. Another important consideration is that LLMs may be constrained as to avoid controversies, and instead are designed to stay as factual as possible, as notably investigated in prior work [14]. We believe this to be strongly linked to the poor performances obtained by the Baseline on the AugLLM dataset.

This study differentiates itself by augmenting French controversial YouTube comments, in order to provide data for a sociological analysis on self-sufficiency. Previous work on textual data augmentation of specific discourse, such as hate speech, has mainly focused on synonym replacement, back-translation and random insertion on the lexical level [15], [16], [17]. LLM-prompting was also explored as a high-performing method for generating new samples in such contexts [8], [18]. However, LLMs show considerable limitations in the representation of social cues involved in controversial speech [13]. The AugLLM method has aligned with these observations by resulting in poorly controversial comments (Section VII-C). Moreover, it has demonstrated that the Mistral LLM produces data unsuitable for controversy classification training. The AugArg method offers an alternative by extending previous work on cross-over combined with a detailed understanding of argumentative structure. By doing so, it aims to result in more semantically coherent partitioning in comparison with random splits implemented in previous work [6], [19]. The AugArg method showed to produce substantial amount of samples, multiplying by 5 the number of comments in our initial corpus, and more representative controversial data. The obtained AugArg dataset allowed for the improvement of model performances in controversy classification, and lessened the bias towards the underrepresented class. Both AugLLM and AugArg, independent methods, were implemented to obtain a broader range of augmented data, and demonstrated methods tailored on controversy markers to be more efficient
in representing controversial text.

## E. Conclusion and Future Work

We believe that possible perspectives for the AugArg method include a stronger identification of argumentative components, possibly with the use of models and techniques from the Argumentation Mining field. This could lead to a better compatibility between components before recombination, and therefore improve the whole coherence in augmented data. The label inheritance hypotheses, of which the results in Section VII questioned or proved the efficiency, represent an interesting lead for further investigation as well. Furthermore, we established the existence of textual repetition in the AugArg corpus due to the nature of the *cross-over* technique. Thus, a combination between *cross-over* and *paraphrasing* or *back-translation* comes across as an interesting perspective to modify syntactic structure and wording of the augmented data without altering its meaning. Moreover, expending the AugArg method to other languages or domains is undoubtedly a promising idea to build on in future work.

Regarding the AugLLM method, future work could include an optimization of the prompt, a performance study on other LLMs and the implementation of a *few-shots, role-based* or *chain-of-thought* protocols. Optimization of the prompt could also be inspired by statistical cues found in controversial comments. For instance, we deem the emojis, URLs, and POS label frequencies to represent possibly interesting constraints for a more fitted prompt. Besides, we observed in our corpus in Section III that emojis could constitute an interesting standpoint in controversy identification methods. Indeed, the link between emojis and emotions has been observed [20], associated by previous work to controversy in text [4], [21].

## VIII. ACKNOWLEDGEMENTS

We acknowledge the contributions of the annotators, whose careful labeling of the dataset made this research possible. This project received financial support from the CNRS through the MITI interdisciplinary program (STAY project), and from the ANR (National Research Agency) through the program Investissements d'avenir #DigitAg, reference ANR-16-CONV-0004. Finally, this work is partially funded by the French National Research Agency (ANR) - HERELLES project (Contract No. ANR-20-CE23-0022).

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