Spain

Depression and anxiety are psychiatric disorders that are observed in many areas of everyday life. For example, these disorders manifest themselves somewhat frequently in texts written by nondiagnosed users in social media. However, detecting users with these conditions is not a straightforward task as they may not explicitly talk about their mental state, and if they do, contextual cues such as immediacy must be taken into account. When available, linguistic flags pointing to probable anxiety or depression could be used by medical experts to write better guidelines and treatments. In this paper, we develop a dataset designed to foster research in depression and anxiety detection in Twitter, framing the detection task as a binary tweet classification problem. We then apply state-of-the-art classification models to this dataset, providing a competitive set of baselines alongside qualitative error analysis. Our results show that language models perform reasonably well, and better than more traditional baselines. Nonetheless, there is clear room for improvement, particularly with unbalanced training sets and in cases where seemingly obvious linguistic cues (keywords) are used counter-intuitively.

COLING 2020

Towards Preemptive Detection of Depression and Anxiety in Twitter

stages 

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for natural language processing and computational linguistics. Often grouped within the field of artificial intelligence, but actually pre-dating the development of artificial intelligence, advances in computational linguistics and natural language processing are now some of the major drivers behind the use of artificial intelligence for commercial and social applications – for example, on-line search, machine translation and with voice-assisted conversational devices.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes, but also in industrial research departments and in technological startups. COLING conferences provide opportunities for all these communities to showcase their exciting developments.

technical paper

We present a study employing various techniques of text mining to explore and compare two different online forums focusing on depression: (1) the subreddit r/depression (over 60 million tokens), a large, open social media platform and (2) Beyond Blue (almost 5 million tokens), a professionally curated and moderated depression forum from Australia. We are interested in how the language and the content on these platforms differ from each other. We scrape both forums for a specific period. Next to general methods of computational text analysis, we focus on sentiment analysis, topic modeling and the distribution of word categories to analyze these forums. Our results indicate that Beyond Blue is generally more positive and that the users are more supportive to each other. Topic modeling shows that Beyond Blue’s users talk more about adult topics like finance and work while topics shaped by school or college terms are more prevalent on r/depression. Based on our findings we hypothesize that the professional curation and moderation of a depression forum is beneficial for the discussion in it.

Exploring Online Depression Forums via Text Mining: A Comparison of Reddit and a Curated Online Forum

In this paper, we described our systems for the first and second subtasks of Social Media Mining for Health Applications (SMM4H) shared task in 2020. The two subtasks are automatic classi-fication of medication mentions and adverse effect in tweets. Our systems for both subtasks are based on Robustly optimized BERT approach (RoBERTa) and our previous work at SMM4H’19. The best F1-scores achieved by our systems for subtask 1 and 2 were 0.7974 and 0.64 respec-tively, which outperformed the average F1-scores among all teams’ best runs by at least 0.13.

ISLab System for SMM4H Shared Task 2020

This presentation describes neural models developed for the Social Media Mining for Health (SMM4H) 2020 shared tasks. Specifically, we participated in two tasks. We investigate the use of a language representation model BERT pre-trained on a large-scale corpus of 5 million health-related user reviews in English and Russian. The ensemble of neural networks for extraction and normalization of adverse drug reactions ranked first among 7 teams at the SMM4H 2020 Task 3 and obtained a relaxed F1 of 46%. The BERT-based multilingual model for classification of English and Russian tweets that report adverse reactions ranked second among 16 and 7 teams at two first subtasks of the SMM4H 2019 Task 2 and obtained a relaxed F1 of 58% on English tweets and 51% on Russian tweets.

KFU NLP Team at SMM4H 2020 Tasks: Cross-lingual Transfer Learning with Pretrained Language Models for Drug Reactions

Our team participated in the second subtask for Russian language creating
a system to detect adverse drug reaction (ADR) presence in a text. For our submission, we exploited an ensemble model architecture, combining BERT’s extension for Russian language, Logistic Regression and domain-specific preprocessing pipeline. Our system was ranked first among others, achieving F-score of 0.51.

BERT implementation for detecting adverse drug effects mentions in Russian

The vast amount of data on social media presents significant opportunities and challenges for utilizing it as a resource for health informatics. The fifth iteration of the Social Media Mining for Health Applications (#SMM4H) shared tasks sought to advance the use of Twitter data (tweets) for pharmacovigilance, toxicovigilance, and epidemiology of birth defects. In addition to re-runs of three tasks, #SMM4H 2020 included new tasks for detecting adverse effects of medications in French and Russian tweets, characterizing chatter related to prescription medication abuse, and detecting self reports of birth defect pregnancy outcomes. The five tasks required methods for binary classification, multi-class classification, and named entity recognition (NER). With 29 teams and a total of 130 system submissions, participation in the #SMM4H shared tasks continues to grow.

Overview of the #SMM4H 2020 Shared Tasks

Prior work in Argument Mining frequently alludes to its potential applications in automatic de-bating systems. Despite this focus, almost no datasets or models exist which apply natural language processing techniques to problems found within competitive formal debate. To remedy this, we present the DebateSum dataset1. DebateSum consists of 187,386 unique pieces of evidence with corresponding argument and extractive summaries. DebateSum was made using data compiled by competitors within the National Speech and Debate Association over a 7-year period. We train several transformer summarization models to benchmark summarization performance on DebateSum. We also introduce a set of fasttext word-vectors trained on DebateSum called debate2vec. Finally, we present a search engine for this dataset which is utilized extensively by members of the National Speech and Debate Association today. The DebateSum search engine is available to the public here: http://www.debate.cards

DebateSum: A large-scale argument mining and summarization dataset

Habernal and Gurevych (2016) in their feasibility study of Arfgument Mining on User Generated Content (UGC) indicated the major challenge facing the field: there seems to be a big gap between the substantial argumentation research and the limited use NLP applications seem to make of these. This also leads to a myriad of annotation approaches, which either start from scratch (with a proprietory scheme which is rarely motivated in terms of the theories underlying it) or apply an existing scheme (Stephen Toulmin being a popular choice for many different text genres, ranging from more formal texts (legal documents) to more informal data like forum posts). The pilot study into the annotation of Dutch Facebook comments (for topics, aspects, stance, argumentativeness and claim) we present was motivated by our hypothesis that the divide between the unpredictable nature of the data and the traditionally restrictive definitions of concepts like argumentativeness may be bridged by gaining more knowledge on the great variety of linguistic expressions of argumentation in user comments. We motivate some of the theoretical principles underlying our annotation guidelines as well as illustrate some of the annotation difficulties which have prompted the adjustments of our initial guidelines.

Annotating topics, stance, argumentativeness and claims in Dutch social media comments: A pilot study

Debate portals and similar web platforms constitute one of the main text sources in computational argumentation research and its applications. While the corpora built upon these sources are rich of argumentatively relevant content and structure, they also include text that is irrelevant, or even detrimental, to their purpose. In this paper, we present a precision-oriented approach to detecting such irrelevant text in a semi-supervised way. Given a few seed examples, the approach automatically learns basic lexical patterns of relevance and irrelevance and then incrementally bootstraps new patterns from sentences matching the patterns. In the existing args.me corpus with 400k argumentative texts, our approach detects almost 87k irrelevant sentences, at a precision of 0.97 according to manual evaluation. With low effort, the approach can be adapted to other web argument corpora, providing a generic way to improve corpus quality.

Semi-Supervised Cleansing of Web Argument Corpora

Sentiment and stance are two important concepts for the analysis of arguments. We propose to add another perspective to the analysis, namely moral sentiment. We argue that moral values are crucial for ideological debates and can thus add useful information for argument mining. In the paper, we present different models for automatically predicting moral sentiment in debates and evaluate them on a manually annotated test set. We then apply our models to investigate how moral values in arguments relate to argument quality, stance, and audience reactions.

Exploring Morality in Argumentation

Computational Argumentation in general and Argument Mining in particular are important research fields. In previous works, many of the challenges to automatically extract and to some degree reason over natural language arguments were addressed. The tools to extract argument units are increasingly available and further open problems can be addressed. In this work, we are presenting the task of Aspect-Based Argument Mining (ABAM), with the essential subtasks of Aspect Term Extraction (ATE) and Nested Segmentation (NS). At the first instance, we create and release an annotated corpus with aspect information on the token-level. We consider aspects as the main point(s) argument units are addressing. This information is important for further downstream tasks such as argument ranking, argument summarization and generation, as well as the search for counter-arguments on the aspect-level. We present several experiments using state-of-the-art supervised architectures and demonstrate their performance for both of the subtasks. The annotated benchmark is available at https://github.com/trtm/ABAM.

Towards Preemptive Detection of Depression and Anxiety in Twitter

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Exploring Online Depression Forums via Text Mining: A Comparison of Reddit and a Curated Online Forum

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Towards Preemptive Detection of Depression and Anxiety in Twitter

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Next from COLING 2020

Exploring Online Depression Forums via Text Mining: A Comparison of Reddit and a Curated Online Forum

Similar lecture

RoBERTa for detecting medications on Twitter

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads