Identifying causal associations in tweets using deep learning: Use case on diabetes-related tweets from 2017-2021

by   Adrian Ahne, et al.

Objective: Leveraging machine learning methods, we aim to extract both explicit and implicit cause-effect associations in patient-reported, diabetes-related tweets and provide a tool to better understand opinion, feelings and observations shared within the diabetes online community from a causality perspective. Materials and Methods: More than 30 million diabetes-related tweets in English were collected between April 2017 and January 2021. Deep learning and natural language processing methods were applied to focus on tweets with personal and emotional content. A cause-effect-tweet dataset was manually labeled and used to train 1) a fine-tuned Bertweet model to detect causal sentences containing a causal association 2) a CRF model with BERT based features to extract possible cause-effect associations. Causes and effects were clustered in a semi-supervised approach and visualised in an interactive cause-effect-network. Results: Causal sentences were detected with a recall of 68 dataset. A CRF model with BERT based features outperformed a fine-tuned BERT model for cause-effect detection with a macro recall of 68 sentences with cause-effect associations. "Diabetes" was identified as the central cluster followed by "Death" and "Insulin". Insulin pricing related causes were frequently associated with "Death". Conclusions: A novel methodology was developed to detect causal sentences and identify both explicit and implicit, single and multi-word cause and corresponding effect as expressed in diabetes-related tweets leveraging BERT-based architectures and visualised as cause-effect-network. Extracting causal associations on real-life, patient reported outcomes in social media data provides a useful complementary source of information in diabetes research.


page 7

page 10

page 14

page 31


Using natural language processing to extract health-related causality from Twitter messages

Twitter messages (tweets) contain various types of information, which in...

Causal-BERT : Language models for causality detection between events expressed in text

Causality understanding between events is a critical natural language pr...

Knowledge-based Extraction of Cause-Effect Relations from Biomedical Text

We propose a knowledge-based approach for extraction of Cause-Effect (CE...

Fuzzy Stochastic Timed Petri Nets for Causal properties representation

Imagery is frequently used to model, represent and communicate knowledge...

Automatically Detecting Self-Reported Birth Defect Outcomes on Twitter for Large-scale Epidemiological Research

In recent work, we identified and studied a small cohort of Twitter user...

Sarcasm Detection in Tweets with BERT and GloVe Embeddings

Sarcasm is a form of communication in whichthe person states opposite of...

IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre-trained Autoregressive Language Model

In this paper, we describe our shared task submissions for Subtask 2 in ...

Please sign up or login with your details

Forgot password? Click here to reset