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Dynamics of cascades on burstiness-controlled temporal networks
Burstiness, the tendency of interaction events to be heterogeneously distributed in time, is critical to information diffusion in physical and social systems. However, an...-
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Interaction Strength Analysis to Model Retweet Cascade Graphs
Tracking information diffusion is a non-trivial task and it has been widely studied across different domains and platforms. The advent of social media has led to even more... -
The role of bot squads in the political propaganda on Twitter
Nowadays, Social Media are a privileged channel for news spreading, information exchange, and fact checking. Unexpectedly for many users, automated accounts, known as social...-
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Towards better social crisis data with HERMES Hybrid sensing for EmeRgency Ma...
People involved in mass emergencies increasingly publish information-rich contents in Online Social Networks (OSNs), thus acting as a distributed and resilient network of... -
A decade of social bot detection
Bots increasingly tamper with political elections and economic discussions. Tracing trends in detection strategies and key suggestions on how to win the fight.-
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Measuring What Counts The case of Rumour Stance Classification
Stance classification can be a powerful tool for understanding whether and which users believe in online rumours. The task aims to automatically predict the stance of replies...-
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Measuring the Impact of Readability Features in Fake News Detection
The proliferation of fake news is a current issue that influences a number of important areas of society, such as politics, economy and health. In the Natural Language...-
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Comparing Topic-Aware Neural Networks for Bias Detection of News
The commercial pressure on media has increasingly dominated the institutional rules of news media, and consequently, more and more sensational and dramatized frames and biases...-
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Grounds for Trust. Essential Epistemic Opacity and Computational Reliabilism
Several philosophical issues in connection with computer simulations rely on the assumption that results of simulations are trustworthy. Examples of these include the debate... -
How the machine thinks. Understanding opacity in machine learning algorithms
This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud... -
Evaluating local explanation methods on ground truth
Evaluating local explanation methods is a difficult task due to the lack of a shared and universally accepted definition of explanation. In the literature, one of the most...-
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Machine Learning Explainability Via Microaggregation and Shallow Decision Trees
Artificial intelligence (AI) is being deployed in missions that are increasingly critical for human life. To build trust in AI and avoid an algorithm-based authoritarian... -
Explanation in artificial intelligence. Insights from the social sciences
There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to provide more transparency to their algorithms.... -
Modeling Adversarial Behavior Against Mobility Data Privacy
Privacy risk assessment is a crucial issue in any privacy-aware analysis process. Traditional frameworks for privacy risk assessment systematically generate the assumed...-
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The PGM-index a fully-dynamic compressed learned index with provable worst-ca...
We present the first learned index that supports predecessor, range queries and updates within provably efficient time and space bounds in the worst case. In the (static)...-
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Seeing without knowing. Limitations of transparency and its application to al...
Models for understanding and holding systems accountable have long rested upon ideals and logics of transparency. Being able to see a system is sometimes equated with being able... -
Solving the Black Box Problem. A Normative Framework for Explainable Artifici...
Many of the computing systems programmed using Machine Learning are opaque: it is difficult to know why they do what they do or how they work. Explainable Artificial... -
Toward Accountable Discrimination Aware Data Mining
"Big Data" and data-mined inferences are affecting more and more of our lives, and concerns about their possible discriminatory effects are growing. Methods for... -
GLocalX-From Local to Global Explanations of Black Box AI Models
Artificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and...-
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Fair Prediction with Disparate Impact A Study of Bias in Recidivism Predictio...
Recidivism prediction instruments (RPIs) provide decision-makers with an assessment of the likelihood that a criminal defendant will reoffend at a future point in time....