Identifying and intercepting health misinformation on Reddit dermatology forums with artificially intelligent bots using natural language processing (Preprint)

2021 ◽  
Author(s):  
Monique B. Sager ◽  
Aditya M. Kashyap ◽  
Mila Tamminga ◽  
Sadhana Ravoori ◽  
Christopher Callison-Burch ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
The United States ◽  
Test Accuracy ◽  
Limited Data ◽  
Test Environment ◽  
Data Set ◽  
Inappropriate Care ◽  
Processing Techniques

BACKGROUND Reddit, the fifth most popular website in the United States, boasts a large and engaged user base on its dermatology forums where users crowdsource free medical opinions. Unfortunately, much of the advice provided is unvalidated and could lead to inappropriate care. Initial testing has shown that artificially intelligent bots can detect misinformation on Reddit forums and may be able to produce responses to posts containing misinformation. OBJECTIVE To analyze the ability of bots to find and respond to health misinformation on Reddit’s dermatology forums in a controlled test environment. METHODS Using natural language processing techniques, we trained bots to target misinformation using relevant keywords and to post pre-fabricated responses. By evaluating different model architectures across a held-out test set, we compared performances. RESULTS Our models yielded data test accuracies ranging from 95%-100%, with a BERT fine-tuned model resulting in the highest level of test accuracy. Bots were then able to post corrective pre-fabricated responses to misinformation. CONCLUSIONS Using a limited data set, bots had near-perfect ability to detect these examples of health misinformation within Reddit dermatology forums. Given that these bots can then post pre-fabricated responses, this technique may allow for interception of misinformation. Providing correct information, even instantly, however, does not mean users will be receptive or find such interventions persuasive. Further work should investigate this strategy’s effectiveness to inform future deployment of bots as a technique in combating health misinformation. CLINICALTRIAL N/A

scholarly journals Using of Natural Language Processing Techniques in Suicide Research

2017 ◽  
Vol 1 (2) ◽  
pp. 89 ◽  
Author(s):  
Azam Orooji ◽  
Mostafa Langarizadeh
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Medical Information ◽  
Inclusion Criteria ◽  
Data Set ◽  
Completed Suicide ◽  
Teenagers And Young Adults ◽  
Processing Techniques ◽  
The Web

It is estimated that each year many people, most of whom are teenagers and young adults die by suicide worldwide. Suicide receives special attention with many countries developing national strategies for prevention. Since, more medical information is available in text, Preventing the growing trend of suicide in communities requires analyzing various textual resources, such as patient records, information on the web or questionnaires. For this purpose, this study systematically reviews recent studies related to the use of natural language processing techniques in the area of people’s health who have completed suicide or are at risk. After electronically searching for the PubMed and ScienceDirect databases and studying articles by two reviewers, 21 articles matched the inclusion criteria. This study revealed that, if a suitable data set is available, natural language processing techniques are well suited for various types of suicide related research.

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

scholarly journals A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity

AERA Open ◽  
2021 ◽  
Vol 7 ◽  
pp. 233285842110286
Author(s):  
Kylie L. Anglin ◽  
Vivian C. Wong ◽  
Arielle Boguslav
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Intervention Implementation ◽  
Proof Of Concept ◽  
Coaching Intervention ◽  
Processing Techniques ◽  
Teacher Coaching ◽  
The Impact

Though there is widespread recognition of the importance of implementation research, evaluators often face intense logistical, budgetary, and methodological challenges in their efforts to assess intervention implementation in the field. This article proposes a set of natural language processing techniques called semantic similarity as an innovative and scalable method of measuring implementation constructs. Semantic similarity methods are an automated approach to quantifying the similarity between texts. By applying semantic similarity to transcripts of intervention sessions, researchers can use the method to determine whether an intervention was delivered with adherence to a structured protocol, and the extent to which an intervention was replicated with consistency across sessions, sites, and studies. This article provides an overview of semantic similarity methods, describes their application within the context of educational evaluations, and provides a proof of concept using an experimental study of the impact of a standardized teacher coaching intervention.

scholarly journals A Study of the Effects of the COVID-19 Pandemic on the Experience of Back Pain Reported on Twitter® in the United States: A Natural Language Processing Approach

2021 ◽  
Vol 18 (9) ◽  
pp. 4543
Author(s):  
Krzysztof Fiok ◽  
Waldemar Karwowski ◽  
Edgar Gutierrez ◽  
Maham Saeidi ◽  
Awad M. Aljuaid ◽  
...  
Keyword(s):  
United States ◽  
Natural Language Processing ◽  
Back Pain ◽  
Natural Language ◽  
Language Processing ◽  
The United States ◽  
Daily Routine ◽  
Body Movements ◽  
Data Source ◽  
Twitter Users

The COVID-19 pandemic has changed our lifestyles, habits, and daily routine. Some of the impacts of COVID-19 have been widely reported already. However, many effects of the COVID-19 pandemic are still to be discovered. The main objective of this study was to assess the changes in the frequency of reported physical back pain complaints reported during the COVID-19 pandemic. In contrast to other published studies, we target the general population using Twitter as a data source. Specifically, we aim to investigate differences in the number of back pain complaints between the pre-pandemic and during the pandemic. A total of 53,234 and 78,559 tweets were analyzed for November 2019 and November 2020, respectively. Because Twitter users do not always complain explicitly when they tweet about the experience of back pain, we have designed an intelligent filter based on natural language processing (NLP) to automatically classify the examined tweets into the back pain complaining class and other tweets. Analysis of filtered tweets indicated an 84% increase in the back pain complaints reported in November 2020 compared to November 2019. These results might indicate significant changes in lifestyle during the COVID-19 pandemic, including restrictions in daily body movements and reduced exposure to routine physical exercise.

A Natural-Language-Processing-Based Procedure for Generating Distractors for Multiple-Choice Questions

2021 ◽  
pp. 016327872110469
Author(s):  
Peter Baldwin ◽  
Janet Mee ◽  
Victoria Yaneva ◽  
Miguel Paniagua ◽  
Jean D’Angelo ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Multiple Choice ◽  
Incorrect Response ◽  
The United States ◽  
Choice Test ◽  
Multiple Choice Questions ◽  
Response Options ◽  
Medical Licensing

One of the most challenging aspects of writing multiple-choice test questions is identifying plausible incorrect response options—i.e., distractors. To help with this task, a procedure is introduced that can mine existing item banks for potential distractors by considering the similarities between a new item’s stem and answer and the stems and response options for items in the bank. This approach uses natural language processing to measure similarity and requires a substantial pool of items for constructing the generating model. The procedure is demonstrated with data from the United States Medical Licensing Examination (USMLE®). For about half the items in the study, at least one of the top three system-produced candidates matched a human-produced distractor exactly; and for about one quarter of the items, two of the top three candidates matched human-produced distractors. A study was conducted in which a sample of system-produced candidates were shown to 10 experienced item writers. Overall, participants thought about 81% of the candidates were on topic and 56% would help human item writers with the task of writing distractors.

scholarly journals Recent Advances in Conversational Intelligent Tutoring Systems

AI Magazine ◽  
2013 ◽  
Vol 34 (3) ◽  
pp. 42-54 ◽  
Author(s):  
Vasile Rus ◽  
Sidney D’Mello ◽  
Xiangen Hu ◽  
Arthur Graesser
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Individual Student ◽  
Learning Progressions ◽  
Tutoring Systems ◽  
Recent Advances ◽  
Processing Techniques

We report recent advances in intelligent tutoring systems with conversational dialogue. We highlight progress in terms of macro and microadaptivity. Macroadaptivity refers to a system’s capability to select appropriate instructional tasks for the learner to work on. Microadaptivity refers to a system’s capability to adapt its scaffolding while the learner is working on a particular task. The advances in macro and microadaptivity that are presented here were made possible by the use of learning progressions, deeper dialogue and natural language processing techniques, and by the use of affect-enabled components. Learning progressions and deeper dialogue and natural language processing techniques are key features of DeepTutor, the first intelligent tutoring system based on learning progressions. These improvements extend the bandwidth of possibilities for tailoring instruction to each individual student which is needed for maximizing engagement and ultimately learning.

Applying Natural Language Processing Techniques to Generate Open Data Web APIs Documentation

2020 ◽  
pp. 416-432
Author(s):  
César González-Mora ◽  
Cristina Barros ◽  
Irene Garrigós ◽  
Jose Zubcoff ◽  
Elena Lloret ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Open Data ◽  
Processing Techniques
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