ymoslem/acl-abstracts huggingface dataset:
Enolp musk@SMM4H`22 : Leveraging Pre-trained Language Models for Stance And Premise Classification (2022)

This paper covers our approaches for the Social Media Mining for Health (SMM4H) Shared Tasks 2a and 2b. Apart from the baseline architectures, we experiment with Parts of Speech (PoS), dependency parsing, and Tf-Idf features. Additionally, we perform contrastive pretraining on our best models using a supervised contrastive loss function. In both the tasks, we outperformed the mean and median scores and ranked first on the validation set. For stance classification, we achieved an F1-score of 0.636 using the CovidTwitterBERT model, while for premise classification, we achieved an F1-score of 0.664 using BART-base model on test dataset.
AIR-JPMC@SMM4H`22: Identifying Self-Reported Spanish COVID-19 Symptom Tweets Through Multiple-Model Ensembling (2022)

We present our response to Task 5 of the Social Media Mining for Health Applications (SMM4H) 2022 competition. We share our approach into classifying whether a tweet in Spanish about COVID-19 symptoms pertain to themselves, others, or not at all. Using a combination of BERT based models, we were able to achieve results that were higher than the median result of the competition.
AIR-JPMC@SMM4H`22: BERT + Ensembling = Too Cool: Using Multiple BERT Models Together for Various COVID-19 Tweet Identification Tasks (2022)

This paper presents my submission for Tasks 1 and 2 for the Social Media Mining of Health (SMM4H) 2022 Shared Tasks competition. I first describe the background behind each of these tasks, followed by the descriptions of the various subtasks of Tasks 1 and 2, then present the methodology. Through model ensembling, this methodology was able to achieve higher results than the mean and median of the competition for the classification tasks.
CAISA@SMM4H`22: Robust Cross-Lingual Detection of Disease Mentions on Social Media with Adversarial Methods (2022)

We propose adversarial methods for increasing the robustness of disease mention detection on social media. Our method applies adversarial data augmentation on the input and the embedding spaces to the English BioBERT model. We evaluate our method in the SocialDisNER challenge at SMM4H`22 on an annotated dataset of disease mentions in Spanish tweets. We find that both methods outperform a heuristic vocabulary-based baseline by a large margin. Additionally, utilizing the English BioBERT model shows a strong performance and outperforms the data augmentation methods even when applied to the Spanish dataset, which has a large amount of data, while augmentation methods show a significant advantage in a low-data setting.
OFU@SMM4H`22: Mining Advent Drug Events Using Pretrained Language Models (2022)

We describe in this paper our proposed systems for the Social Media Mining for Health 2022 shared task 1. In particular, we participated in the three sub-tasks, tasks that aim at extracting and processing Adverse Drug Events. We investigate different transformer-based pretrained models we fine-tuned on each task and proposed some improvement on the task of entity normalization.
CompLx@SMM4H`22: In-domain pretrained language models for detection of adverse drug reaction mentions in English tweets (2022)

The paper describes the system that team CompLx developed for sub-task 1a of the Social Media Mining for Health 2022 ({\#}SMM4H) Shared Task. We finetune a RoBERTa model, a pretrained, transformer-based language model, on a provided dataset to classify English tweets for mentions of Adverse Drug Reactions (ADRs), i.e. negative side effects related to medication intake. With only a simple finetuning, our approach achieves competitive results, significantly outperforming the average score across submitted systems. We make the model checkpoints and code publicly available. We also create a web application to provide a user-friendly, readily accessible interface for anyone interested in exploring the model`s capabilities.
The SocialDisNER shared task on detection of disease mentions in health-relevant content from social media: methods, evaluation, guidelines and corpora (2022)

There is a pressing need to exploit health-related content from social media, a global source of data where key health information is posted directly by citizens, patients and other healthcare stakeholders. Use cases of disease related social media mining include disease outbreak/surveillance, mental health and pharmacovigilance. Current efforts address the exploitation of social media beyond English. The SocialDisNER task, organized as part of the SMM4H 2022 initiative, has applied the LINKAGE methodology to select and annotate a Gold Standard corpus of 9,500 tweets in Spanish enriched with disease mentions generated by patients and medical professionals. As a complementary resource for teams participating in the SocialDisNER track, we have also created a large-scale corpus of 85,000 tweets, where in addition to disease mentions, other medical entities of relevance (e.g., medications, symptoms and procedures, among others) have been automatically labelled. Using these large-scale datasets, co-mention networks or knowledge graphs were released for each entity pair type. Out of the 47 teams registered for the task, 17 teams uploaded a total of 32 runs. The top-performing team achieved a very competitive 0.891 f-score, with a system trained following a continue pre-training strategy. We anticipate that the corpus and systems resulting from the SocialDisNER track might further foster health related text mining of social media content in Spanish and inspire disease detection strategies in other languages.
Romanian micro-blogging named entity recognition including health-related entities (2022)

This paper introduces a manually annotated dataset for named entity recognition (NER) in micro-blogging text for Romanian language. It contains gold annotations for 9 entity classes and expressions: persons, locations, organizations, time expressions, legal references, disorders, chemicals, medical devices and anatomical parts. Furthermore, word embeddings models computed on a larger micro-blogging corpus are made available. Finally, several NER models are trained and their performance is evaluated against the newly introduced corpus.
The Best of Both Worlds: Combining Engineered Features with Transformers for Improved Mental Health Prediction from Reddit Posts (2022)

In recent years, there has been increasing interest in the application of natural language processing and machine learning techniques to the detection of mental health conditions (MHC) based on social media data. In this paper, we aim to improve the state-of-the-art (SoTA) detection of six MHC in Reddit posts in two ways: First, we built models leveraging Bidirectional Long Short-Term Memory (BLSTM) networks trained on in-text distributions of a comprehensive set of psycholinguistic features for more explainable MHC detection as compared to black-box solutions. Second, we combine these BLSTM models with Transformers to improve the prediction accuracy over SoTA models. In addition, we uncover nuanced patterns of linguistic markers characteristic of specific MHC.
Leveraging Social Media as a Source for Clinical Guidelines: A Demarcation of Experiential Knowledge (2022)

In this paper we present a procedure to extract posts that contain experiential knowledge from Facebook discussions in Dutch, using automated filtering, manual annotations and machine learning. We define guidelines to annotate experiential knowledge and test them on a subset of the data. After several rounds of (re-)annotations, we come to an inter-annotator agreement of K=0.69, which reflects the difficulty of the task. We subsequently discuss inclusion and exclusion criteria to cope with the diversity of manifestations of experiential knowledge relevant to guideline development.
COVID-19-related Nepali Tweets Classification in a Low Resource Setting (2022)

Billions of people across the globe have been using social media platforms in their local languages to voice their opinions about the various topics related to the COVID-19 pandemic. Several organizations, including the World Health Organization, have developed automated social media analysis tools that classify COVID-19-related tweets to various topics. However, these tools that help combat the pandemic are limited to very few languages, making several countries unable to take their benefit. While multi-lingual or low-resource language-specific tools are being developed, there is still a need to expand their coverage, such as for the Nepali language. In this paper, we identify the eight most common COVID-19 discussion topics among the Twitter community using the Nepali language, set up an online platform to automatically gather Nepali tweets containing the COVID-19-related keywords, classify the tweets into the eight topics, and visualize the results across the period in a web-based dashboard. We compare the performance of two state-of-the-art multi-lingual language models for Nepali tweet classification, one generic (mBERT) and the other Nepali language family-specific model (MuRIL). Our results show that the models' relative performance depends on the data size, with MuRIL doing better for a larger dataset. The annotated data, models, and the web-based dashboard are open-sourced at \url{https://github.com/naamiinepal/covid-tweet-classification}.
SMM4H 2022 Task 2: Dataset for stance and premise detection in tweets about health mandates related to COVID-19 (2022)

This paper is an organizers' report of the competition on argument mining systems dealing with English tweets about COVID-19 health mandates. This competition was held within the framework of the SMM4H 2022 shared tasks. During the competition, the participants were offered two subtasks: stance detection and premise classification. We present a manually annotated corpus containing 6,156 short posts from Twitter on three topics related to the COVID-19 pandemic: school closures, stay-at-home orders, and wearing masks. We hope the prepared dataset will support further research on argument mining in the health field.
Overview of the Seventh Social Media Mining for Health Applications (\#SMM4H) Shared Tasks at COLING 2022 (2022)

For the past seven years, the Social Media Mining for Health Applications ({\#}SMM4H) shared tasks have promoted the community-driven development and evaluation of advanced natural language processing systems to detect, extract, and normalize health-related information in public, user-generated content. This seventh iteration consists of ten tasks that include English and Spanish posts on Twitter, Reddit, and WebMD. Interest in the {\#}SMM4H shared tasks continues to grow, with 117 teams that registered and 54 teams that participated in at least one task{---}a 17.5{\%} and 35{\%} increase in registration and participation, respectively, over the last iteration. This paper provides an overview of the tasks and participants' systems. The data sets remain available upon request, and new systems can be evaluated through the post-evaluation phase on CodaLab.
Toward a Semi-Automated Scoping Review of Virtual Human Smiles (2022)

Smiles are a fundamental facial expression for successful human-agent communication. The growing number of publications in this domain presents an opportunity for future research and design to be informed by a scoping review of the extant literature. This semi-automated review expedites the first steps toward the mapping of Virtual Human (VH) smile research. This paper contributes an overview of the status quo of VH smile research, identifies research streams through cluster analysis, identifies prolific authors in the field, and provides evidence that a full scoping review is needed to synthesize the findings in the expanding domain of VH smile research. To enable collaboration, we provide full access to the refined VH smile dataset, key word and author word clouds, as well as interactive evidence maps.
Are You Smiling When I Am Speaking? (2022)

The aim of this study is to investigate conversational feedbacks that contain smiles and laughs. Firstly, we propose a statistical analysis of smiles and laughs used as generic and specific feedbacks in a corpus of French talk-in-interaction. Our results show that smiles of low intensity are preferentially used to produce generic feedbacks while high intensity smiles and laughs are preferentially used to produce specific feedbacks. Secondly, based on a machine learning approach, we propose a hierarchical classification of feedback to automatically predict not only the presence/absence of a smile but, also the type of smiles according to an intensity-scale (low or high).
Gender Differences, Smiling, and Economic Negotiation Outcomes (2022)

Research documents gender differences in nonverbal behavior and negotiation outcomes. Women tend to smile more often than men and men generally perform better in economic negotiation contexts. Among nonverbal behaviors, smiling can serve various social functions, from rewarding or appeasing others to conveying dominance, and could therefore be extremely useful in economic negotiations. However, smiling has hardly been studied in negotiation contexts. Here we examine links between smiling, gender, and negotiation outcomes. We analyze a corpus of video recordings of participant dyads during mock salary negotiations and test whether women smile more than men and if the amount of smiling can predict economic negotiation outcomes. Consistent with existing literature, women smiled more than men. There was no significant relationship between smiling and negotiation outcomes and gender did not predict negotiation performance. Exploratory analyses showed that expected negotiation outcomes, strongly correlated with actual outcomes, tended to be higher for men than for women. Implications for the gender pay gap and future research are discussed.
A Measure of the Smiling Synchrony in the Conversational Face-to-face Interaction Corpus PACO-CHEESE (2022)

The smiling synchrony of the French audio-video conversational corpora {\textquotedblleft}PACO{\textquotedblright} and {\textquotedblleft}Cheese!{\textquotedblright} is investigated. The two corpora merged altogether last 6 hours and are made of 25 face-to-face dyadic interactions annotated following the 5 levels Smiling Intensity Scale proposed by Gironzetti et al. (2016). After introducing new indicators for characterizing synchrony phenomena, we find that almost all the 25 interactions of PACO-CHEESE show a strong and significant smiling synchrony behavior. We investigate in a second step the evolution of the synchrony parameters throughout the interaction. No effect is found and it appears rather that the smiling synchrony is present at the very start of the interaction and remains unchanged throughout the conversation.
Analysis of Co-Laughter Gesture Relationship on RGB Videos in Dyadic Conversation Context (2022)

The development of virtual agents has enabled human-avatar interactions to become increasingly rich and varied. Moreover, an expressive virtual agent i.e. that mimics the natural expression of emotions, enhances social interaction between a user (human) and an agent (intelligent machine). The set of non-verbal behaviors of a virtual character is, therefore, an important component in the context of human-machine interaction. Laughter is not just an audio signal, but an intrinsic relationship of multimodal non-verbal communication, in addition to audio, it includes facial expressions and body movements. Motion analysis often relies on a relevant motion capture dataset, but the main issue is that the acquisition of such a dataset is expensive and time-consuming. This work studies the relationship between laughter and body movements in dyadic conversations between two interlocutors. The body movements were extracted from videos using deep learning based pose estimator model. We found that, in the explored NDC-ME dataset, a single statistical feature (i.e, the maximum value, or the maximum of Fourier transform) of a joint movement weakly correlates with laughter intensity by 30{\%}. However, we did not find a direct correlation between audio features and body movements. We discuss about the challenges to use such dataset for the audio-driven co-laughter motion synthesis task.
Intergroup Bias in Smile Discrimination in Autism (2022)

Genuine and posed smiles are important social cues (Song, Over, {\&} Carpenter, 2016). Autistic individuals struggle to reliably differentiate between them (Blampied, Johnston, Miles, {\&} Liberty, 2010; Boraston, Corden, Miles, Skuse, {\&} Blakemore, 2008), which may contribute to their difficulties in understanding others' mental states. An intergroup bias has been found in non-autistic adults in identifying genuine from posed smiles (Young, 2017). This is the first study designed to investigate if autistic individuals would show a different pattern when differentiating smiles for in-groups and out-groups. Fifty-nine autistic adults were compared with forty non-autistic adults, matched on sex, age and nonverbal IQ. Roughly, half of each group were further randomly separated into two groups with a minimal group paradigm (adapted from Howard {\&} Rothbart, 1980). There was no real difference between the groups, participants were primed to believe they were more similar to their in-groups. The ability to distinguish smiles was assessed on a 7-point Likert scale. We found both autism and non-autism groups rated genuine smiles more genuine than posed smiles and in-groups more genuine than out-groups. Even though both groups identified themselves more as in-group than out-group members, autistic individuals were less likely to than non-autistic individuals. However, autistic participants generally rated smiles as less genuine than non-autistic counterparts. These results indicate that autistic adults are capable of identifying genuine smiles from posed smiles, unlike previous findings; but they may be less convinced of the genuineness of others, which may affect their social communication thereafter. Importantly, autistic adults were equally influenced by social intergroup biases which has the potential to be used in interventions to alleviate their social difficulties in daily lives.
Inhalation Noises as Endings of Laughs in Conversational Speech (2022)

In this study we investigate the role of inhalation noises at the end of laughter events in two conversational corpora that provide relevant annotations. A re-annotation of the categories for laughter, silence and inbreath noises enabled us to see that inhalation noises terminate laughter events in the majority of all inspected laughs with a duration comparable to inbreath noises initiating speech phases. This type of corpus analysis helps to understand the mechanisms of audible respiratory activities in speaking vs. laughing in conversations.
Are There Any Body-movement Differences between Women and Men When They Laugh? (2022)

Smiling differences between men and women have been studied in psychology. Women smile more than men although the expressiveness of women is not universally more across all facial actions. There are also body movement differences between women and men. For example, more open-body postures were reported for men, but are there any body-movement differences between men and women when they laugh? To investigate this question, we study body-movement signals extracted from recorded laughter videos using a deep learning pose estimation model. Initial results showed a higher Fourier Transform amplitude of thorax and shoulder movements for females while males had a higher Fourier transform amplitude of Elbow movement. The differences were not limited to a small frequency range but covered most of the frequency spectrum. However, further investigations are still needed.
Laughter During Cooperative and Competitive Games (2022)

This exploratory study investigates the extent to which social context influences the frequency of laughter. In a within-subjects design, dyads of strangers played two simple laughter-inducing games in a cooperative and competitive setting, ostensibly to earn money individually and as a team. We examined the frequency of laughs produced in both settings. The analysis revealed that, the effects of cooperative versus competitive framing interacted with the game. Specifically, when playing a general knowledge quiz, participants tended to laugh more in the cooperative than in the competitive setting. However, the opposite was true when participants were asked to find a specific number of poker chips under time pressure. During this task participants laughed more in a competitive than in the cooperative setting. Further analyses revealed that familiarity with the task affected the amount of laughter differently for each of the two tasks. Playing the second round of the poker chips task was associated with a significant decreases in laughter frequency compared to the first round. This effect was less marked for the general knowledge quiz, where increased familiarity with the task in the second round led to more laughs in the cooperative, but not competitive setting. Together, the results highlight the flexibility of laughter as an interaction signal and illustrate the challenges of studying laughter in naturalistic settings.
Synthesis for the Kinematic Control of Identity in Sign Language (2022)

Sign Language (SL) animations generated from motion capture (mocap) of real signers convey critical information about their identity. It has been suggested that this information is mostly carried by statistics of the movements kinematics. Manipulating these statistics in the generation of SL movements could allow controlling the identity of the signer, notably to preserve anonymity. This paper tests this hypothesis by presenting a novel synthesis algorithm that manipulates the identity-specific statistics of mocap recordings. The algorithm produced convincing new versions of French Sign Language discourses, which accurately modulated the identity prediction of a machine learning model. These results open up promising perspectives toward the automatic control of identity in the motion animation of virtual signers.
Analysis of Torso Movement for Signing Avatar Using Deep Learning (2022)

Avatars are virtual or on-screen representations of a human used in various roles for sign language display, including translation and educational tools. Though the ability of avatars to portray acceptable sign language with believable human-like motion has improved in recent years, many still lack the naturalness and supporting motions of human signing. Such details are generally not included in the linguistic annotation. Nevertheless, these motions are highly essential to displaying lifelike and communicative animations. This paper presents a deep learning model for use in a signing avatar. The study focuses on coordinating torso movements and other human body parts. The proposed model will automatically compute the torso rotation based on the avatar`s wrist positions. The resulting motion can improve the user experience and engagement with the avatar.