French Twitter Research Articles

Right-Wing Twitter Users in France Exhibit Growing Homophily Compared With Left and Center Users

The purpose of this research is to enhance our understanding of homophilic behaviors—where individuals prefer to associate with others like themselves—on Twitter, particularly focusing on how these behaviors vary across different political affiliations. While a general increase in homophily is a well-documented phenomenon in social networks, its expression within the diverse political contexts on Twitter remains underexplored. This study seeks to understand how political alignment influences homophily and its possible role in reinforcing extreme viewpoints. In particular, the research examines the patterns of interaction within political communities on French Twitter, delineating between right-wing, left-wing, and centrist groups. The findings reveal a significant pattern: right-wing groups demonstrate a marked increase in homophily over time, suggesting a stronger predilection for in-group interaction when compared with their centrist and left-wing counterparts. In addition, the study monitors the evolution of follower networks among these political clusters, providing insights into the shifting popularity of political parties within each group. By analyzing the social connections within these clusters, the study offers a detailed perspective on the dynamics of political homophily on Twitter and how it evolves, informing our understanding of political communication and group behavior in the digital age.

Users' Reactions to Announced Vaccines Against COVID-19 Before Marketing in France: Analysis of Twitter Posts.

Within a few months, the COVID-19 pandemic had spread to many countries and had been a real challenge for health systems all around the world. This unprecedented crisis has led to a surge of online discussions about potential cures for the disease. Among them, vaccines have been at the heart of the debates and have faced lack of confidence before marketing in France. This study aims to identify and investigate the opinions of French Twitter users on the announced vaccines against COVID-19 through sentiment analysis. This study was conducted in 2 phases. First, we filtered a collection of tweets related to COVID-19 available on Twitter from February 2020 to August 2020 with a set of keywords associated with vaccine mistrust using word embeddings. Second, we performed sentiment analysis using deep learning to identify the characteristics of vaccine mistrust. The model was trained on a hand-labeled subset of 4548 tweets. A set of 69 relevant keywords were identified as the semantic concept of the word "vaccin" (vaccine in French) and focused mainly on conspiracies, pharmaceutical companies, and alternative treatments. Those keywords enabled us to extract nearly 350,000 tweets in French. The sentiment analysis model achieved 0.75 accuracy. The model then predicted 16% of positive tweets, 41% of negative tweets, and 43% of neutral tweets. This allowed us to explore the semantic concepts of positive and negative tweets and to plot the trends of each sentiment. The main negative rhetoric identified from users' tweets was that vaccines are perceived as having a political purpose and that COVID-19 is a commercial argument for the pharmaceutical companies. Twitter might be a useful tool to investigate the arguments for vaccine mistrust because it unveils political criticism contrasting with the usual concerns on adverse drug reactions. As the opposition rhetoric is more consistent and more widely spread than the positive rhetoric, we believe that this research provides effective tools to help health authorities better characterize the risk of vaccine mistrust.

Combination of GRU and CNN Deep Learning Models for Sentiment Analysis on French Customer Reviews Using XLNet Model

Sentiment analysis is the task of detecting opinions of people from text using techniques of natural language processing. It is critical in assisting businesses in actively improving their company strategy and better understanding client feedback on their products. Recently, the researchers have shown that deep learning models, namely convolutional neural network (CNN), recurrent neural networks (RNNs), and contextualized transformer-based word embeddings, give hopeful results for extracting sentiment from text. Withal, bidirectional RNN utilizes two directions of RNN to better extract long-term dependences, CNN has the benefit of high-level features extracting, and it may not examine a sequence of correlations efficiently. In addition, the transformer-based word embeddings are the computational resources needed to fine-tune to solve the problem of overfitting on small datasets. For that, we propose in this work a combination of different RNNs models [e.g., long short-term memory (LSTM), bidirectional LSTM, and gated recurrent unit (GRU)] and CNN, using different word embeddings (MultiFiT, XLNet, and CamemBERT). The experimental results show that the combination of GRU and CNN with XLNet has an apparent improvement upon the state of the art on three French datasets: 1) French Amazon Customer Reviews, 2) AlloCiné Dataset, and 3) French Twitter Sentiment Analysis, with 96.5%, 90.1%, and 89.6% accuracies in decreasing order, respectively.

Official new terms in the age of social media: the story ofhashtagon French Twitter

AbstractAs other chapters in this special issue demonstrate, social media proposes widely available data for sociolinguistic analysis. Twitter is an ideal resource to implement variationist approaches regarding regional differences, features specific to gender, and metrics of social media influence. At the same time, official intervention on language use, while somewhat studied in other corpora, is less explored on Twitter. French shows a long tradition of purist and prescriptive ideologies, embodied by the Académie française in France and the Office québécois de la langue française in Québec. The injection of recommended terminology aimed to eradicate foreign influence often has questionable success rate, especially in such an informal setting as Twitter. This article thus investigates lexical variation, in particular, the implantation of official new French translationsmot-dièseandmot-clicbetween 2010 and 2016 that are meant to replace the English wordhashtag. Results corroborate previous findings on the lacklustre implantation of the prescribed terms, while also revealing that users in Québec are more inclined to adapt them. Furthermore, diffusion online reflects face-to-face patterns that is cascading spread from large urban areas to smaller cities.

L’Arbre qui cache la forêt: on the sexualization of the term beurette, Gilles Kepel’s travel writing, and the French mission civilisatrice

ABSTRACT In June 2020, the hasthtag #TF1raciste began trending on French Twitter after the synopsis of a TV series broadcast on France’s first television channel described a female character of Maghrebi descent as a beurette. As the term was being debated on many a media outlet, sociologists explained that beurette is embedded in a colonial rhetoric that fetishizes Oriental women and noted that its contemporary usage is mostly associated with the pornographic industry, suggesting a discriminatory if niche representation of Arab women in France. Through an analysis of the travel writing of Gilles Kepel, France’s leading expert on Islamism, this article argues that this representation is not restricted to the marginal production of adult content but that it is instead found in a public representation of Islam that, behind its denunciation of Islamism, hides a French yearning for the colonial mission civilisatrice.

“#JeSuisSirCornflakes”: Racialization and resemiotization in French nationalist Twitter

Abstract After the 2016 spelling reforms deleted the accent circumflex from some French vowels, on right-wing French Twitter, the circonflexe reappeared in the center of the French flag – echoing the flag of Vichy France. Tweets with the hashtag #JeSuisCirconflexe resemiotized the accent circumflex as icon of a lost Frenchness, or voiced the racial other in a colonial faux pidgin to frame them as illiterate and brutish. Drawing on research on resemiotization (Leppänen, Sirpa, Samu Kytölä, Henna Jousmäki, Saija Peuronen & Elina Westinen. 2014. Entextualization and resemiotization as resources for identification in social media. In The language of social media, 112–136. London: Palgrave Macmillan) and raciolinguistics (Flores, Nelson & Jonathan Rosa. 2015. Undoing appropriateness: Raciolinguistic ideologies and language diversity in education. Harvard Educational Review, 85(2). 149–171), this article explores how constructions of mock youth French use raciolinguistic tropes to imagine a language of social decline, connecting linguistic purism to racist myths of white genocide and the great replacement. Despite this, youth invert the imagination of their illiteracy, using playful language and satirizing white speech (Rosa, Jonathan. 2016b. Standardization, racialization, languagelessness: Raciolinguistic ideologies across communicative contexts. Journal of Linguistic Anthropology 26(2). 162–183) to contest French nationalism – reframing #JeSuisCirconflexe as #JeSuisSirCornflakes.

Optimal Proxy Selection for Socioeconomic Status Inference on Twitter

Individual socioeconomic status inference from online traces is a remarkably difficult task. While current methods commonly train predictive models on incomplete data by appending socioeconomic information of residential areas or professional occupation profiles, little attention has been paid to how well this information serves as a proxy for the individual demographic trait of interest when fed to a learning model. Here we address this question by proposing three different data collection and combination methods to first estimate and, in turn, infer the socioeconomic status of French Twitter users from their online semantics. We assess the validity of each proxy measure by analyzing the performance of our prediction pipeline when trained on these datasets. Despite having to rely on different user sets, we find that training our model on professional occupation provides better predictive performance than open census data or remote sensed expert annotation of habitual environments. Furthermore, we release the tools we developed in the hope it will provide a generalizable framework to estimate socioeconomic status of large numbers of Twitter users as well as contribute to the scientific discussion on social stratification and inequalities.

Unsupervised graph-based pattern extraction for multilingual emotion classification

The connected society we live in today has allowed online users to willingly share opinions on an unprecedented scale. Motivated by the advent of mass opinion sharing, it is then crucial to devise algorithms that efficiently identify the emotions expressed within the opinionated content. Traditional opinion-based classifiers require extracting high-dimensional feature representations, which become computationally expensive to process and can misrepresent or deteriorate the accuracy of a classifier. In this paper, we propose an unsupervised graph-based approach for extracting Twitter-specific emotion-bearing patterns to be used as features. By utilizing a more representative list of patterns, as features, we improved the precision and recall of a given emotion classification task. Due to its novel bootstrapping process, the full system is also adaptable to different domains and languages. The experimented results demonstrate that the extracted patterns are effective in identifying emotions for English, Spanish, and French Twitter streams. We also provide detailed experiments and offer an extended version of our algorithm to support the classification of Indonesian microblog posts. Overall, our empirical experimented results demonstrate that the proposed approach bears desirable characteristics such as accuracy, generality, adaptability, minimal supervision, and coverage.

Event detection, tracking, and visualization in Twitter: a mention-anomaly-based approach

The ever-growing number of people using Twitter makes it a valuable source of timely information. However, detecting events in Twitter is a difficult task, because tweets that report interesting events are overwhelmed by a large volume of tweets on unrelated topics. Existing methods focus on the textual content of tweets and ignore the social aspect of Twitter. In this paper we propose MABED (i.e. mention-anomaly-based event detection), a novel statistical method that relies solely on tweets and leverages the creation frequency of dynamic links (i.e. mentions) that users insert in tweets to detect significant events and estimate the magnitude of their impact over the crowd. MABED also differs from the literature in that it dynamically estimates the period of time during which each event is discussed, rather than assuming a predefined fixed duration for all events. The experiments we conducted on both English and French Twitter data show that the mention-anomaly-based approach leads to more accurate event detection and improved robustness in presence of noisy Twitter content. Qualitatively speaking, we find that MABED helps with the interpretation of detected events by providing clear textual descriptions and precise temporal descriptions. We also show how MABED can help understanding users' interest. Furthermore, we describe three visualizations designed to favor an efficient exploration of the detected events.