https://pure.mpg.de/pubman/item/item_1819068_3/component/file_1840695/MPI-I-2007-5-003.pdf

YAGO: A Large Ontology from Wikipedia and WordNet

The field of machine learning has matured to the point where many sophisticated learning approaches can be applied to practical applications. Thus it is of critical importance that researchers have the proper tools to evaluate learning approaches and understand the underlying issues. This book examines various aspects of the evaluation process with an emphasis on classification algorithms. The authors describe several techniques for classifier performance assessment, error estimation and resampling, obtaining statistical significance as well as selecting appropriate domains for evaluation. They also present a unified evaluation framework and highlight how different components of evaluation are both significantly interrelated and interdependent. The techniques presented in the book are illustrated using R and WEKA facilitating better practical insight as well as implementation.Aimed at researchers in the theory and applications of machine learning, this book offers a solid basis for conducting performance evaluations of algorithms in practical settings.

Evaluating Learning Algorithms: A Classification Perspective

The large number of potential applications from bridging web data with knowledge bases have led to an increase in the entity linking research. Entity linking is the task to link entity mentions in text with their corresponding entities in a knowledge base. Potential applications include information extraction, information retrieval, and knowledge base population. However, this task is challenging due to name variations and entity ambiguity. In this survey, we present a thorough overview and analysis of the main approaches to entity linking, and discuss various applications, the evaluation of entity linking systems, and future directions.

/pdf/entity-linking-with-a-knowledge-base-issues-techniques-and-2ds9mn4o8g.pdf

Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions

The Nature Of Emotion Fundamental Questions

Background: Since the beginning of December 2019, the coronavirus disease (COVID-19) has spread rapidly around the world, which has led to increased discussions across online platforms. These conversations have also included various conspiracies shared by social media users. Amongst them, a popular theory has linked 5G to the spread of COVID-19, leading to misinformation and the burning of 5G towers in the United Kingdom. The understanding of the drivers of fake news and quick policies oriented to isolate and rebate misinformation are keys to combating it.
Objective: The aim of this study is to develop an understanding of the drivers of the 5G COVID-19 conspiracy theory and strategies to deal with such misinformation.
Methods: This paper performs a social network analysis and content analysis of Twitter data from a 7-day period (Friday, March 27, 2020, to Saturday, April 4, 2020) in which the #5GCoronavirus hashtag was trending on Twitter in the United Kingdom. Influential users were analyzed through social network graph clusters. The size of the nodes were ranked by their betweenness centrality score, and the graph’s vertices were grouped by cluster using the Clauset-Newman-Moore algorithm. The topics and web sources used were also examined.
Results: Social network analysis identified that the two largest network structures consisted of an isolates group and a broadcast group. The analysis also revealed that there was a lack of an authority figure who was actively combating such misinformation. Content analysis revealed that, of 233 sample tweets, 34.8% (n=81) contained views that 5G and COVID-19 were linked, 32.2% (n=75) denounced the conspiracy theory, and 33.0% (n=77) were general tweets not expressing any personal views or opinions. Thus, 65.2% (n=152) of tweets derived from nonconspiracy theory supporters, which suggests that, although the topic attracted high volume, only a handful of users genuinely believed the conspiracy. 

This paper also shows that fake news websites were the most popular web source shared by users; although, YouTube videos were also shared. The study also identified an account whose sole aim was to spread the conspiracy theory on Twitter.
Conclusions: The combination of quick and targeted interventions oriented to delegitimize the sources of fake information is key to reducing their impact. Those users voicing their views against the conspiracy theory, link baiting, or sharing humorous tweets inadvertently raised the profile of the topic, suggesting that policymakers should insist in the efforts of isolating opinions that are based on fake news. Many social media platforms provide users with the ability to report inappropriate content, which should be used. This study is the first to analyze the 5G conspiracy theory in the context of COVID-19 on Twitter offering practical guidance to health authorities in how, in the context of a pandemic, rumors may be combated in the future.

/pdf/covid-19-and-the-5g-conspiracy-theory-social-network-3589sws8pz.pdf

COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data.

We compare three popular techniques of rating content: the ubiquitous five star rating, the less used pairwise comparison, and the recently introduced (in crowdsourcing) magnitude estimation approach. Each system has specific advantages and disadvantages, in terms of required user effort, achievable user preference prediction accuracy and number of ratings required. 
We design an experiment where the three techniques are compared in an unbiased way. We collected 39'000 ratings on a popular crowdsourcing platform, allowing us to release a dataset that will be useful for many related studies on user rating techniques.

Pairwise, Magnitude, or Stars: What's the Best Way for Crowds to Rate?

/pdf/e-bart-jointly-predicting-and-explaining-truthfulness-42tlzvf8hk.pdf

E-BART: Jointly Predicting and Explaining Truthfulness.

A dominant narrative of the past decade is that algorithms contribute to a misinformed and segregated society. Perhaps paradoxically, algorithms are often sought as solutions to such problems. We describe a significant emerging trend away from this techno-solutionist approach that seeks to create and understand a new paradigm: a productive interplay between algorithms and people. Two relevant test cases are being explored in our region: the first addresses a new framework to tackle misinformation by assisting fact-checkers with computational methods; the second seeks new models to understand how search engines deliver personalised search results when little or no algorithmic personalisation exists.

/pdf/human-ai-cooperation-to-tackle-misinformation-and-thetp0lv.pdf

Human-AI Cooperation to Tackle Misinformation and Polarization

Format inconsistency is one of the most frequently appearing data quality issues encountered during data cleaning. Existing automated approaches commonly lack applicability and generalisability, while approaches with human inputs typically require specialized skills such as writing regular expressions. This paper proposes a novel hybrid human-machine system, namely “Data-Scanner-4C”, which leverages crowdsourcing to address syntactic format inconsistencies in a single column effectively. We first ask crowd workers to create examples from single-column data through “data selection” and “result validation” tasks. Then, we propose and use a novel rule-based learning algorithm to infer the regular expressions that propagate formats from created examples to the entire column. Our system integrates crowdsourcing and algorithmic format extraction techniques in a single workflow. Having human experts write regular expressions is no longer required, thereby reducing both the time as well as the opportunity for error. We conducted experiments through both synthetic and real-world datasets, and our results show how the proposed approach is applicable and effective across data types and formats.

Human-in-the-loop Regular Expression Extraction for Single Column Format Inconsistency

/pdf/human-in-the-loop-systems-for-truthfulness-a-study-of-human-145jp4xtx1.pdf

Gianluca Demartini

Papers

Pairwise, Magnitude, or Stars: What's the Best Way for Crowds to Rate?

E-BART: Jointly Predicting and Explaining Truthfulness.

Human-AI Cooperation to Tackle Misinformation and Polarization

Human-in-the-loop Regular Expression Extraction for Single Column Format Inconsistency

Human-in-the-Loop Systems for Truthfulness: A Study of Human and Machine Confidence.