Report on CLEF 2018

Bellot, Patrice; Cappellato, Linda; Ferro, Nicola; Mothe, Josiane; Murtagh, Fionn; Nie, Jian‐Yun; SanJuan, Eric; Soulier, Laure; Trabelsi, Chiraz

doi:10.1145/3308774.3308785

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…The eRisk forum each year organizes a new task around a specific disorder; for instance, in 2017 and 2018, the shared task focused on depression [9], [10], and in 2019 and 2020, there were tasks on predicting anorexia and self-harm tendencies [11], [21]. For each task, a dataset is created using Reddit posts selected from the authors' self-report of having a mental health disorder.…”

Section: Related Workmentioning

confidence: 99%

Extracting Mental Health Indicators From English and Spanish Social Media: A Machine Learning Approach

Villa-Pérez,

Trejo,

Moin

et al. 2023

IEEE Access

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This study examines the communications of English-and Spanish-speaking Twitter users through traditional and deep learning algorithms to automatically recognize whether they live with one of nine mental health conditions. We created two datasets in English and Spanish. The "diagnosed" set comprises the timeline of 1,500 users who explicitly reported in one or more of their posts having been diagnosed with one of the following: ADHD, Anxiety, Autism, Bipolar, Depression, Eating disorders, OCD, PTSD, and Schizophrenia. The "control" set comprises the timeline of 1,700 randomly selected users who had not disclosed a diagnosis. We extracted a variety of text features from the collected data, such as n-grams, q-grams, Part-of-speech (POS) tags, topic modeling, Linguistic Inquiry and Word Count (LIWC), and word embeddings, and trained traditional machine-learning and deep learning classifiers for two tasks: binary classification, to distinguish between diagnosed and non-diagnosed users, and multiclass classification, to identify the specific diagnosis. Overall, XGBoost and convolutional neural network (CNN) performed the best in the two classification tasks. Moreover, lexical attributes based on n-grams and q-grams are the ones that performed well in both datasets. Using our collected datasets, for binary classification, we achieved an AUC of 0.835 on the Spanish Twitter dataset using n-grams of words from one to three (UBT) and 0.846 on the English Twitter dataset with a 5-gram characters (C5) model. In multiclass classification, we obtained an AUC of 0.712 and 0.697 in the Spanish and English Twitter datasets, respectively.

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Section: Related Workmentioning

confidence: 99%

Extracting Mental Health Indicators From English and Spanish Social Media: A Machine Learning Approach

Villa-Pérez,

Trejo,

Moin

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

Evaluating stance annotation of Twitter data

Simaki

Seitanidi

Paradis

2022

RiCL

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Taking stance towards any topic, event or idea is a common phenomenon on Twitter and social media in general. Twitter users express their opinions about different matters and assess other people’s opinions in various discursive ways. The identification and analysis of the linguistic ways that people use to take different stances leads to a better understanding of the language and user behaviour on Twitter. Stance is a multidimensional concept involving a broad range of related notions such as modality, evaluation and sentiment. In this study, we annotate data from Twitter using six notional stance categories ––contrariety, hypotheticality, necessity, prediction, source of knowledge and uncertainty––¬¬ following a comprehensive annotation protocol including inter-coder reliability measurements. The relatively low agreement between annotators highlighted the challenges that the task entailed, which made us question the inter-annotator agreement score as a reliable measurement of annotation quality of notional categories. The nature of the data, the difficulty of the stance annotation task and the type of stance categories are discussed, and potential solutions are suggested

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Report on CLEF 2018

Cited by 2 publications

References 14 publications

Extracting Mental Health Indicators From English and Spanish Social Media: A Machine Learning Approach

Extracting Mental Health Indicators From English and Spanish Social Media: A Machine Learning Approach

Evaluating stance annotation of Twitter data

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