Did You Really Just Have a Heart Attack?

Karisani, Payam; Agichtein, Eugene

doi:10.1145/3178876.3186055

Cited by 35 publications

(9 citation statements)

References 18 publications

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“…We used the eRisk2019 training users to obtain the golden sentences. Following the approach by Karisani and Agichtein (2018), three experts in the field conducted the annotation process. The number of golden sentences was low, averaging 35 per symptom.…”

Section: Few Manually Labelled Sentences As Queriesmentioning

confidence: 99%

“…Before the annotation process, we removed all supplementary metadata to avoid bias in the annotators, such as the severity option label (0 − 3) of the user who wrote the sentence. We followed the same annotation procedure as Karisani and Agichtein (2018) to validate the annotation outcomes. This procedure consisted of two phases: 1) First, an initial annotator answered the following question in a binary setting (Positive/Negative): Does the sentence refer to the symptom, and the user talks about himself/herself (first person)?.…”

Section: A Manual Dataset and Annotation Processmentioning

confidence: 99%

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Semantic Similarity Models for Depression Severity Estimation

Pérez,

Warikoo,

Wang

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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Depressive disorders constitute a severe public health issue worldwide. However, public health systems have limited capacity for case detection and diagnosis. In this regard, the widespread use of social media has opened up a way to access public information on a large scale. Computational methods can serve as support tools for rapid screening by exploiting this user-generated social media content. This paper presents an efficient semantic pipeline to study depression severity in individuals based on their social media writings. We select test user sentences for producing semantic rankings over an index of representative training sentences corresponding to depressive symptoms and severity levels. Then, we use the sentences from those results as evidence for predicting symptoms severity. For that, we explore different aggregation methods to answer one of four Beck Depression Inventory (BDI-II) options per symptom. We evaluate our methods on two Reddit-based benchmarks, achieving improvement over state of the art in terms of measuring depression level 1 . * Work done during an internship at UKP Lab. 1 gitlab.irlab.org/anxo.pvila/semantic-4-depression.

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Section: Few Manually Labelled Sentences As Queriesmentioning

confidence: 99%

Section: A Manual Dataset and Annotation Processmentioning

confidence: 99%

Semantic Similarity Models for Depression Severity Estimation

Pérez,

Warikoo,

Wang

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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“…One focus is on the analysis of Twitter with regards to pharmacovigilance. Other topics include the extraction of adverse drug reactions (Nikfarjam et al, 2015;Cocos et al, 2017), monitoring public health (Paul and Dredze, 2012;Choudhury et al, 2013;, and detecting personal health mentions (Yin et al, 2015;Karisani and Agichtein, 2018).…”

Section: Biomedical Text Miningmentioning

confidence: 99%

Claim Detection in Biomedical Twitter Posts

Wührl¹,

Klinger²

2021

Proceedings of the 20th Workshop on Biomedical Language Processing

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Social media contains unfiltered and unique information, which is potentially of great value, but, in the case of misinformation, can also do great harm. With regards to biomedical topics, false information can be particularly dangerous. Methods of automatic fact-checking and fake news detection address this problem, but have not been applied to the biomedical domain in social media yet. We aim to fill this research gap and annotate a corpus of 1200 tweets for implicit and explicit biomedical claims (the latter also with span annotations for the claim phrase). With this corpus, which we sample to be related to COVID-19, measles, cystic fibrosis, and depression, we develop baseline models which detect tweets that contain a claim automatically. Our analyses reveal that biomedical tweets are densely populated with claims (45 % in a corpus sampled to contain 1200 tweets focused on the domains mentioned above). Baseline classification experiments with embedding-based classifiers and BERT-based transfer learning demonstrate that the detection is challenging, however, shows acceptable performance for the identification of explicit expressions of claims. Implicit claim tweets are more challenging to detect.

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“…Personal Health Mentions For S4, the training data consists of data from one disease domain, namely influenza, in two contexts: having a flu infection and getting a flu vaccination. To improve generalisability, we supplemented this data with six labelled data sets from different disease domains (Karisani and Agichtein, 2018). We refer to this combined data set as S4+.…”

Section: Additional Datamentioning

confidence: 99%

“…2 Personal Health Mention Extraction The goal of Subtask 4 (S4) is to identify tweets that are personal health mentions, i.e. posts that mention a person who is affected as well as their specific condition (Karisani and Agichtein, 2018), as opposed to posts discussing health issues in general. Generalisability to both future data and different health domains is evaluated by including data from the same domain collected years after the training data, as well as data from entirely different disease domain.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning for Health-related Twitter Data

Dirkson¹,

Verberne²

2019

Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop &Amp; Shared Task

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Transfer learning is promising for many NLP applications, especially in tasks with limited labeled data. This paper describes the methods developed by team TMRLeiden for the 2019 Social Media Mining for Health Applications (SMM4H) Shared Task. Our methods use state-of-the-art transfer learning methods to classify, extract and normalise adverse drug effects (ADRs) and to classify personal health mentions from health-related tweets. The code and fine-tuned models are publicly available. 1

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Did You Really Just Have a Heart Attack?

Cited by 35 publications

References 18 publications

Semantic Similarity Models for Depression Severity Estimation

Semantic Similarity Models for Depression Severity Estimation

Claim Detection in Biomedical Twitter Posts

Transfer Learning for Health-related Twitter Data

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