Florian Kunneman1,2, Mattijs Lambooij3, Albert Wong3, Antal van den Bosch4,5, Liesbeth Mollema3. 1. Radboud University, Erasmusplein 1, Nijmegen, 6525, HT, The Netherlands. f.a.kunneman@vu.nl. 2. Vrije Universiteit Amsterdam, De Boelelaan 1111, Amsterdam, 1081, HV, The Netherlands. f.a.kunneman@vu.nl. 3. Dutch National Institute for Public Health and Environment, Antonie van Leeuwenhoeklaan 9, Bilthoven, 3721, MA, The Netherlands. 4. Radboud University, Erasmusplein 1, Nijmegen, 6525, HT, The Netherlands. 5. KNAW Meertens Institute, PO Box 10855, Amsterdam, 1001, EW, The Netherlands.
Abstract
BACKGROUND: We developed a system to automatically classify stance towards vaccination in Twitter messages, with a focus on messages with a negative stance. Such a system makes it possible to monitor the ongoing stream of messages on social media, offering actionable insights into public hesitance with respect to vaccination. At the moment, such monitoring is done by means of regular sentiment analysis with a poor performance on detecting negative stance towards vaccination. For Dutch Twitter messages that mention vaccination-related key terms, we annotated their stance and feeling in relation to vaccination (provided that they referred to this topic). Subsequently, we used these coded data to train and test different machine learning set-ups. With the aim to best identify messages with a negative stance towards vaccination, we compared set-ups at an increasing dataset size and decreasing reliability, at an increasing number of categories to distinguish, and with different classification algorithms. RESULTS: We found that Support Vector Machines trained on a combination of strictly and laxly labeled data with a more fine-grained labeling yielded the best result, at an F1-score of 0.36 and an Area under the ROC curve of 0.66, considerably outperforming the currently used sentiment analysis that yielded an F1-score of 0.25 and an Area under the ROC curve of 0.57. We also show that the recall of our system could be optimized to 0.60 at little loss of precision. CONCLUSION: The outcomes of our study indicate that stance prediction by a computerized system only is a challenging task. Nonetheless, the model showed sufficient recall on identifying negative tweets so as to reduce the manual effort of reviewing messages. Our analysis of the data and behavior of our system suggests that an approach is needed in which the use of a larger training dataset is combined with a setting in which a human-in-the-loop provides the system with feedback on its predictions.
BACKGROUND: We developed a system to automatically classify stance towards vaccination in Twitter messages, with a focus on messages with a negative stance. Such a system makes it possible to monitor the ongoing stream of messages on social media, offering actionable insights into public hesitance with respect to vaccination. At the moment, such monitoring is done by means of regular sentiment analysis with a poor performance on detecting negative stance towards vaccination. For Dutch Twitter messages that mention vaccination-related key terms, we annotated their stance and feeling in relation to vaccination (provided that they referred to this topic). Subsequently, we used these coded data to train and test different machine learning set-ups. With the aim to best identify messages with a negative stance towards vaccination, we compared set-ups at an increasing dataset size and decreasing reliability, at an increasing number of categories to distinguish, and with different classification algorithms. RESULTS: We found that Support Vector Machines trained on a combination of strictly and laxly labeled data with a more fine-grained labeling yielded the best result, at an F1-score of 0.36 and an Area under the ROC curve of 0.66, considerably outperforming the currently used sentiment analysis that yielded an F1-score of 0.25 and an Area under the ROC curve of 0.57. We also show that the recall of our system could be optimized to 0.60 at little loss of precision. CONCLUSION: The outcomes of our study indicate that stance prediction by a computerized system only is a challenging task. Nonetheless, the model showed sufficient recall on identifying negative tweets so as to reduce the manual effort of reviewing messages. Our analysis of the data and behavior of our system suggests that an approach is needed in which the use of a larger training dataset is combined with a setting in which a human-in-the-loop provides the system with feedback on its predictions.
Entities:
Keywords:
Sentiment analysis; Social media; Vaccination
Authors: Quyen G To; Kien G To; Van-Anh N Huynh; Nhung T Q Nguyen; Diep T N Ngo; Stephanie J Alley; Anh N Q Tran; Anh N P Tran; Ngan T T Pham; Thanh X Bui; Corneel Vandelanotte Journal: Int J Environ Res Public Health Date: 2021-04-12 Impact factor: 3.390
Authors: Eden Engel-Rebitzer; Daniel C Stokes; Zachary F Meisel; Jonathan Purtle; Rebecca Doyle; Alison M Buttenheim Journal: JMIR Infodemiology Date: 2022-02-18