The overturn of Roe v. Wade radically changed abortion access within the United States leaving women to navigate new financial, legal, and logistical challenges in managing their reproductive health needs. Reporting on findings from co-design workshops with participants from Indiana (a state with an abortion ban) and New York (where abortion is accessible), we investigate how women envision care in response to ongoing legal and medical uncertainty. Drawing together techno-feminist scholarship on care and reproductive health, in this paper we highlight several "entangled" design stories of anxiety and fear in navigating diminished healthcare services, as well as resistance and hope. Our findings prompt critical reflections for HCI on the role of health technology amid a world in which reproductive health, and medicine at large, is often a site of political contestation and conflict. Care-in-Retrograde re-orients a techno-utopian and future-oriented view of health technology to consider design work amid healthcare trajectories of disruption and reversal.

We present a qualitative interview study that examines what happens when things do not go as planned with digital contraception. Through an analysis of 27 interviews with ongoing users of digital contraception at the time of the study, we convey participants’ accounts of their experiences regarding unplanned pregnancies or use of emergency contraception to avoid an unplanned pregnancy. Our analysis considers participants’ sense-making processes, and notably how they attended to questions of risk and responsibility. Finally, we depict how these participants came to continue using digital contraception after these experiences. Our study connects to ongoing conversations on technological failures in personal informatics and safety-critical systems. We emphasise that failure and success should not be used as a binary classification of long-term users’ relationships with self-tracking technology, which are intimate and critical. Rather, the sustained relation with an intimate technology is composed by several ‘failures’ which are interpreted, acted upon, and, ultimately, overcome.

Low levels of health literacy concerning Alzheimer's Disease and related dementias (ADRD) impact African American/Black communities access to appropriate ADRD care. Additionally, a legacy of mistrust in medical research due to systemic racism, has resulted in insufficient participation in ADRD clinical trials among African American/Black adults. This study explores the potential of generative AI to improve ADRD literacy and encourage participation in clinical trials among African American/Black older adults. We designed a mobile health intervention featuring AI-driven conversational agents - a chatbot and a voice assistant - specifically developed for this population. We tested the quality of the intervention using heuristics methodology adapted to the target population along with inputs from African American/ Black medical professionals and UX designers. Key findings highlight the unique needs of the African American/Black communities for culturally relevant content that is accessible to users with varying language levels and tailored to users' geographical location. Concerning the interaction, high levels of personalization and control over the interaction can promote the use of the tool, by minimizing complexity and maximizing accessibility. These findings show the novel contribution offered by our study in the domain of designing health technology with generative AI, particularly LLMS, for African American/Black communities.

This paper describes the LiITA Knowledge Base of interoperable linguistic resources for Italian.By adhering to the Linked Open Data principles, LiITA ensures and facilitates interoperability between distributed resources. The paper outlines the lemma-centered architecture of the Knowledge Base and details its core component: the Lemma Bank, a collection of Italian lemmas designed to interlink distributed lexical and textual resources.

Historically, transgender people of color (TPOC) have been silenced in white trans spaces for not fitting into transnormativity - the typical white, binary, skinny, and privileged image of trans people, and for raising concerns related to race, culture, and ethnicity. Social media and online communities serve as supportive spaces for transgender (shortened to trans) individuals; however, trans people of color require even more support combating systematic oppression, managing increased levels of discrimination, and navigating their cultural backgrounds. In order to understand how TPOC use social media, we explore the experiences of TPOC on Reddit. We used the Reddit API to obtain Reddit posts from four prominent transgender subreddits (r/ftm, r/mtf, r/trans, and r/Non-Binary) which included the phrase "people of color" or the abbreviation "POC", resulting in a total of 145 posts and 2867 comments. Thematic analysis was then used to identify three themes of discussion - alienation, support, and existing in physical spaces, which informed our design considerations. Experiences shared in the Reddit posts indicated that TPOC feel overshadowed by white trans individuals in online communities and desire to build connections with other TPOC both online and in person. We propose design recommendations for both Reddit as a platform and subreddit moderators that regulate online trans communities to encourage growing networks among TPOC, improve communication among users and moderators, and design spaces that center POC voices within subreddits, all of which provide a much more supportive online environment for TPOC.

Alzheimer's Disease and related dementia (ADRD) is prevalent in one in nine individuals age 65 or above, and it has a 65% higher risk of incidence for African American/Black adults. With an aging population in the United States and persisting healthcare inequities for African American/Black adults, our research aims to explore design requirements of a digital health platform for delivering culturally relevant content that informs African Americans/Black adults (45 years and older) about brain health and participation in clinical ADRD studies. We conducted seven focus groups (n = 44) to collect information on facilitators and barriers to brain health literacy and participation in clinical ADRD research, followed by seven participatory design workshops (n = 44) to collaboratively develop solutions for improving brain health literacy and participation in clinical ADRD research. Our findings provide insights into incorporating community into accessible, technological design for reducing brain health disparities for African American/Black adults.

Silvia Casola, Simona Frenda, Soda Marem Lo, Erhan Sezerer, Antonio Uva, Valerio Basile, Cristina Bosco, Alessandro Pedrani, Chiara Rubagotti, Viviana Patti, Davide Bernardi. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.

The paper describes a dataset composed of two sub-corpora from two different sources in Italian. The QUEEREOTYPES corpus includes social media texts regarding LGBTQIA+ individuals, behaviors, ideology and events. The texts were collected from Facebook and Twitter in 2018 and were annotated for the presence of stereotypes, and orthogonal dimensions (such as hate speech, aggressiveness, offensiveness, and irony in one sub-corpus, and stance in the other). The resource was developed by Natural Language Processing researchers together with activists from an Italian LGBTQIA+ not-for-profit organization. The creation of the dataset allows the NLP community to study stereotypes against marginalized groups, individuals and, ultimately, to develop proper tools and measures to reduce the online spread of such stereotypes. A test for the robustness of the language resource has been performed by means of 5-fold cross-validation experiments. Finally, text classification experiments have been carried out with a fine-tuned version of AlBERTo (a BERT-based model pre-trained on Italian tweets) and mBERT, obtaining good results on the task of stereotype detection, suggesting that stereotypes towards different targets might share common traits.

Simona Frenda, Alessandro Pedrani, Valerio Basile, Soda Marem Lo, Alessandra Teresa Cignarella, Raffaella Panizzon, Cristina Marco, Bianca Scarlini, Viviana Patti, Cristina Bosco, Davide Bernardi. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Silvia Casola, Soda Lo, Valerio Basile, Simona Frenda, Alessandra Cignarella, Viviana Patti, Cristina Bosco. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.

The European Language Grid enables researchers and practitioners to easily distribute and use NLP resources and models, such as corpora and classifiers. We describe in this paper how, during the course of our EVALITA4ELG project, we have integrated datasets and systems for the Italian language. We show how easy it is to use the integrated systems, and demonstrate in case studies how seamless the application of the platform is, providing Italian NLP for everyone.

This paper presents an in-depth investigation of the effectiveness of dependency-based syntactic features on the irony detection task in a multilingual perspective (English, Spanish, French and Italian).It focuses on the contribution from syntactic knowledge, exploiting linguistic resources where syntax is annotated according to the Universal Dependencies scheme.Three distinct experimental settings are provided.In the first, a variety of syntactic dependency-based features combined with classical machine learning classifiers are explored.In the second scenario, two well-known types of word embeddings are trained on parsed data and tested against gold standard datasets.In the third setting, dependency-based syntactic features are combined into the Multilingual BERT architecture.The results suggest that fine-grained dependency-based syntactic information is informative for the detection of irony.

The recognition of irony is a challenging task in the domain of Sentiment Analysis, and the availability of annotated corpora may be crucial for its automatic processing. In this paper we describe a fine-grained annotation scheme centered on irony, in which we highlight the tokens that are responsible for its activation, (irony activators) and their morpho-syntactic features. As our case study we therefore introduce a recently released Universal Dependencies treebank for Italian which includes ironic tweets: TWITTIRÒ-UD. For the purposes of this study, we enriched the existing annotation in the treebank, with a further level that includes irony activators. A description and discussion of the annotation scheme is provided with a definition of irony activators and the guidelines for their annotation. This qualitative study on the different layers of annotation applied on the same dataset can shed some light on the process of human annotation, and irony annotation in particular, and on the usefulness of this representation for developing computational models of irony to be used for training purposes.

The paper presents a discussion on the main linguistic phenomena of user-generated texts found in web and social media, and proposes a set of annotation guidelines for their treatment within the Universal Dependencies (UD) framework. Given on the one hand the increasing number of treebanks featuring user-generated content, and its somewhat inconsistent treatment in these resources on the other, the aim of this paper is twofold: (1) to provide a short, though comprehensive, overview of such treebanks - based on available literature - along with their main features and a comparative analysis of their annotation criteria, and (2) to propose a set of tentative UD-based annotation guidelines, to promote consistent treatment of the particular phenomena found in these types of texts. The main goal of this paper is to provide a common framework for those teams interested in developing similar resources in UD, thus enabling cross-linguistic consistency, which is a principle that has always been in the spirit of UD.

The Social Web represents nowadays the principal means to support and foster social interactions among people through Web 2.0 technologies. Individuals interact in virtual communities to pursue mutual interests or goals, by exchanging multiple kinds of contents (i.e., textual, acoustic, visual), the so-called User-Generated Content (UGC). In this context, the SeCredISData Special Session is especially devoted at discussing the implications that the analysis of big social data has in tackling open issues related to society from different perspectives. On one side, there is the need to push forward the research on emotion and sentiment, and the investigation of affective cognitive models and their possible integration into intelligent systems. On the other side, it is urgent to address the issue of on-line information credibility assessment, in an era where trusted intermediaries have disappeared and people must rely only on their cognitive capacities to judge information. The Special Session is therefore aimed at promoting the development of models and applications able to tackle these issues.

Jihen Karoui, Farah Benamara, Véronique Moriceau, Viviana Patti, Cristina Bosco, Nathalie Aussenac-Gilles. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. 2017.

This paper describes the SimpleNLG-IT realiser, i.e. the main features of the porting of the SimpleNLG API system (Gatt and Reiter, 2009) to Italian.The paper gives some details about the grammar and the lexicon employed by the system and reports some results about a first evaluation based on a dependency treebank for Italian.A comparison is developed with the previous projects developed for this task for English and French, which is based on the morpho-syntactical differences and similarities between Italian and these languages.

Political debates about a reform may sparkle national controversies, by leading members of the community to polarize their opinions and sentiment about the topic addressed. With the rise of social media like Twitter users are encouraged to voice and share their strong and polarized views and in general people are exposed to broader viewpoints than they were before. The large amount of user-generated social data available is a great opportunity to investigate the communicative behaviors emerging in the context of such political debates and to shed some light on the way communities of users with different roles in the society and different political sentiment interact. In this paper we focussed on communications in Twitter around the reform of marriage in France in 2012 and 2013 - “Le Mariage Pour Tous” - which had been the subject of debate and controversy. We collected a corpus of tweets tagged by the hashtag #mariagepourtous, created to mark the messages about the reform. We applied different kinds of analysis on our dataset based on linguistic and non linguistic features of the observed data in order to investigate the communicative behavior in using subjective and evaluative language on a political topic. The analysis leaded also to reflect on the impact of different typologies of users involved in the virtual debate which included both political messages created by media organizations and by other individual users, from ordinary citizens to politicians or celebrities.