Technologies for extracting and analysing the credibility of health-related online content

The evolution of the Web has led to an improvement in information accessibility. This change has allowed access to more varied content at greater speed, but we must also be aware of the dangers involved. The results offered may be unreliable, inadequate, or of poor quality, leading to misinformation. This can have a greater or lesser impact depending on the domain, but is particularly sensitive when it comes to health-related content. In this thesis, we focus in the development of methods to automatically assess credibility. We also studied the reliability of the new Large Language Models (LLMs) to answer health questions. Finally, we also present a set of tools that might help in the massive analysis of web textual content

Application of the Markov Chain Method in a Health Portal Recommendation System

This study produced a recommendation system that can effectively recommend items on a health portal. Toward this aim, a transaction log that records users’ traversal activities on the Medical College of Wisconsin’s HealthLink, a health portal with a subject directory, was utilized and investigated. This study proposed a mixed-method that included the transaction log analysis method, the Markov chain analysis method, and the inferential analysis method. The transaction log analysis method was applied to extract users’ traversal activities from the log. The Markov chain analysis method was adopted to model users’ traversal activities and then generate recommendation lists for topics, articles, and Q&A items on the health portal. The inferential analysis method was applied to test whether there are any correlations between recommendation lists generated by the proposed recommendation system and recommendation lists ranked by experts. The topics selected for this study are Infections, the Heart, and Cancer. These three topics were the three most viewed topics in the portal. The findings of this study revealed the consistency between the recommendation lists generated from the proposed system and the lists ranked by experts. At the topic level, two topic recommendation lists generated from the proposed system were consistent with the lists ranked by experts, while one topic recommendation list was highly consistent with the list ranked by experts. At the article level, one article recommendation list generated from the proposed system was consistent with the list ranked by experts, while 14 article recommendation lists were highly consistent with the lists ranked by experts. At the Q&A item level, three Q&A item recommendation lists generated from the proposed system were consistent with the lists ranked by experts, while 12 Q&A item recommendation lists were highly consistent with the lists ranked by experts. The findings demonstrated the significance of users’ traversal data extracted from the transaction log. The methodology applied in this study proposed a systematic approach to generating the recommendation systems for other similar portals. The outcomes of this study can facilitate users’ navigation, and provide a new method for building a recommendation system that recommends items at three levels: the topic level, the article level, and the Q&A item level

Advanced Data Mining Techniques for Compound Objects

Knowledge Discovery in Databases (KDD) is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in large data collections. The most important step within the process of KDD is data mining which is concerned with the extraction of the valid patterns. KDD is necessary to analyze the steady growing amount of data caused by the enhanced performance of modern computer systems. However, with the growing amount of data the complexity of data objects increases as well. Modern methods of KDD should therefore examine more complex objects than simple feature vectors to solve real-world KDD applications adequately. Multi-instance and multi-represented objects are two important types of object representations for complex objects. Multi-instance objects consist of a set of object representations that all belong to the same feature space. Multi-represented objects are constructed as a tuple of feature representations where each feature representation belongs to a different feature space. The contribution of this thesis is the development of new KDD methods for the classification and clustering of complex objects. Therefore, the thesis introduces solutions for real-world applications that are based on multi-instance and multi-represented object representations. On the basis of these solutions, it is shown that a more general object representation often provides better results for many relevant KDD applications. The first part of the thesis is concerned with two KDD problems for which employing multi-instance objects provides efficient and effective solutions. The first is the data mining in CAD parts, e.g. the use of hierarchic clustering for the automatic construction of product hierarchies. The introduced solution decomposes a single part into a set of feature vectors and compares them by using a metric on multi-instance objects. Furthermore, multi-step query processing using a novel filter step is employed, enabling the user to efficiently process similarity queries. On the basis of this similarity search system, it is possible to perform several distance based data mining algorithms like the hierarchical clustering algorithm OPTICS to derive product hierarchies. The second important application is the classification and search for complete websites in the world wide web (WWW). A website is a set of HTML-documents that is published by the same person, group or organization and usually serves a common purpose. To perform data mining for websites, the thesis presents several methods to classify websites. After introducing naive methods modelling websites as webpages, two more sophisticated approaches to website classification are introduced. The first approach uses a preprocessing that maps single HTML-documents within each website to so-called page classes. The second approach directly compares websites as sets of word vectors and uses nearest neighbor classification. To search the WWW for new, relevant websites, a focused crawler is introduced that efficiently retrieves relevant websites. This crawler minimizes the number of HTML-documents and increases the accuracy of website retrieval. The second part of the thesis is concerned with the data mining in multi-represented objects. An important example application for this kind of complex objects are proteins that can be represented as a tuple of a protein sequence and a text annotation. To analyze multi-represented objects, a clustering method for multi-represented objects is introduced that is based on the density based clustering algorithm DBSCAN. This method uses all representations that are provided to find a global clustering of the given data objects. However, in many applications there already exists a sophisticated class ontology for the given data objects, e.g. proteins. To map new objects into an ontology a new method for the hierarchical classification of multi-represented objects is described. The system employs the hierarchical structure of the ontology to efficiently classify new proteins, using support vector machines

Web Credibility: Features Exploration and Credibility Prediction

International audienceData Stream Processing (DSP) applications are often modelled as a directed acyclic graph: operators with data streams among them. Inter-operator communications can have a significant impact on the latency of DSP applications, accounting for 86% of the total latency. Despite their impact, there has been relatively little work on optimizing inter-operator communications, focusing on reducing inter-node traffic but not considering inter-process communication (IPC) inside a node, which often generates high latency due to the multiple memory-copy operations. This paper describes the design and implementation of TurboStream, a new DSP system designed specifically to address the high latency caused by inter-operator communications. To achieve this goal, we introduce (1) an improved IPC framework with OSRBuffer, a DSP-oriented buffer, to reduce memory-copy operations and waiting time of each single message when transmitting messages between the operators inside one node, and (2) a coarse-grained scheduler that consolidates operator instances and assigns them to nodes to diminish the inter-node IPC traffic. Using a prototype implementation, we show that our improved IPC framework reduces the end-to-end latency of intra-node IPC by 45.64% to 99.30%. Moreover, TurboStream reduces the latency of DSP by 83.23% compared to JStorm

Automatically applying a credibility appraisal tool to track vaccination-related communications shared on social media

Background: Tools used to appraise the credibility of health information are time-consuming to apply and require context-specific expertise, limiting their use for quickly identifying and mitigating the spread of misinformation as it emerges. Our aim was to estimate the proportion of vaccination-related posts on Twitter are likely to be misinformation, and how unevenly exposure to misinformation was distributed among Twitter users. Methods: Sampling from 144,878 vaccination-related web pages shared on Twitter between January 2017 and March 2018, we used a seven-point checklist adapted from two validated tools to appraise the credibility of a small subset of 474. These were used to train several classifiers (random forest, support vector machines, and a recurrent neural network with transfer learning), using the text from a web page to predict whether the information satisfies each of the seven criteria. Results: Applying the best performing classifier to the 144,878 web pages, we found that 14.4\% of relevant posts to text-based communications were linked to webpages of low credibility and made up 9.2\% of all potential vaccination-related exposures. However, the 100 most popular links to misinformation were potentially seen by between 2 million and 80 million Twitter users, and for a substantial sub-population of Twitter users engaging with vaccination-related information, links to misinformation appear to dominate the vaccination-related information to which they were exposed. Conclusions: We proposed a new method for automatically appraising the credibility of webpages based on a combination of validated checklist tools. The results suggest that an automatic credibility appraisal tool can be used to find populations at higher risk of exposure to misinformation or applied proactively to add friction to the sharing of low credibility vaccination information.Comment: 8 Pages, 5 Figure

An Automatic Ontology Generation Framework with An Organizational Perspective

Ontologies have been known for their powerful semantic representation of knowledge. However, ontologies cannot automatically evolve to reflect updates that occur in respective domains. To address this limitation, researchers have called for automatic ontology generation from unstructured text corpus. Unfortunately, systems that aim to generate ontologies from unstructured text corpus are domain-specific and require manual intervention. In addition, they suffer from uncertainty in creating concept linkages and difficulty in finding axioms for the same concept. Knowledge Graphs (KGs) has emerged as a powerful model for the dynamic representation of knowledge. However, KGs have many quality limitations and need extensive refinement. This research aims to develop a novel domain-independent automatic ontology generation framework that converts unstructured text corpus into domain consistent ontological form. The framework generates KGs from unstructured text corpus as well as refine and correct them to be consistent with domain ontologies. The power of the proposed automatically generated ontology is that it integrates the dynamic features of KGs and the quality features of ontologies