We investigated the differential diffusion of all of the verified true and false news stories distributed on Twitter from 2006 to 2017. The data comprise ~126,000 stories tweeted by ~3 million people more than 4.5 million times. We classified news as true or false using information from six independent fact-checking organizations that exhibited 95 to 98% agreement on the classifications. Falsehood diffused significantly farther, faster, deeper, and more broadly than the truth in all categories of information, and the effects were more pronounced for false political news than for false news about terrorism, natural disasters, science, urban legends, or financial information. We found that false news was more novel than true news, which suggests that people were more likely to share novel information. Whereas false stories inspired fear, disgust, and surprise in replies, true stories inspired anticipation, sadness, joy, and trust. Contrary to conventional wisdom, robots accelerated the spread of true and false news at the same rate, implying that false news spreads more than the truth because humans, not robots, are more likely to spread it.

The spread of true and false news online

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Social Network Analysis

The paper “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification” by Joy Buolamwini and Timnit Gebru, that will be presented at the Conference on Fairness, Accountability, and Transparency (FAT*) in February 2018, evaluates three commercial API-based classifiers of gender from facial images, including IBM Watson Visual Recognition. The study finds these services to have recognition capabilities that are not balanced over genders and skin tones [1]. In particular, the authors show that the highest error involves images of dark-skinned women, while the most accurate result is for light-skinned men.

/pdf/gender-shades-intersectional-accuracy-disparities-in-4qgeu0c1i3.pdf

Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification

With the widespread use of AI systems and applications in our everyday lives, it is important to take fairness issues into consideration while designing and engineering these types of systems. Such systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that the decisions do not reflect discriminatory behavior toward certain groups or populations. We have recently seen work in machine learning, natural language processing, and deep learning that addresses such challenges in different subdomains. With the commercialization of these systems, researchers are becoming aware of the biases that these applications can contain and have attempted to address them. In this survey we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined in order to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and how they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

A Survey on Bias and Fairness in Machine Learning

A plurality of application profiles are obtained, for a plurality of applications. Each of the profiles specifies a list of resources, and requirements for each of the resources, associated with a corresponding one of the applications. Specification of a plurality of constraints associated with the applications is facilitated, as is obtaining a plurality of cost models associated with at least two different kinds of servers on which the applications are to run. A recommended server configuration is generated for running the applications, by formulating and solving a bin packing problem. Each of the at least two different kinds of servers is treated as a bin of a different size, based on its capacity, and has an acquisition cost associated therewith. The size is substantially equal to a corresponding one of the resource requirement as given by a corresponding one of the application profiles. Each of the applications is treated as an item, with an associated size, to be packed into the bins. The bin packing problem develops the recommended server configuration based on reducing a total acquisition cost while satisfying the constraints and the sizes of the applications.

Method, system and computer program product for server selection, application placement and consolidation

Discretization is a crucial preprocessing technique used for a variety of data warehousing and mining tasks. In this paper, we present a novel PCA-based unsupervised algorithm for the discretization of continuous attributes in multivariate data sets. The algorithm leverages the underlying correlation structure in the data set to obtain the discrete intervals and ensures that the inherent correlations are preserved. Previous efforts on this problem are largely supervised and consider only piecewise correlation among attributes. We consider the correlation among continuous attributes and, at the same time, also take into account the interactions between continuous and categorical attributes. Our approach also extends easily to data sets containing missing values. We demonstrate the efficacy of the approach on real data sets and as a preprocessing step for both classification and frequent itemset mining tasks. We show that the intervals are meaningful and can uncover hidden patterns in data. We also show that large compression factors can be obtained on the discretized data sets. The approach is task independent, i.e., the same discretized data set can be used for different data mining tasks. Thus, the data sets can be discretized, compressed, and stored once and can be used again and again.

Toward unsupervised correlation preserving discretization

Rumor is a potentially harmful social phenomenon that has been observed in all human societies in all times Social networking sites provide a platform for the rapid interchange of information and hence, for the rapid dissemination of unsubstantiated claims that are potentially harmful In this paper, we study different methods for combating rumors in social networks actuated by the realization that authoritarian methods for fighting rumor have largely failed Our major insight is that in situations where populations do not answer to the same authority, it is the trust that individuals place in their friends that must be leveraged to fight rumor In other words, rumor is best combated by something which acts like itself, a message which spreads from one individual to another We call such messages anti-rumors We study three natural anti-rumor processes to counter the rumor and present mean field equations that characterize the system Several metrics are proposed to capture the properties of rumor and anti-rumor processes The metrics are geared to capture temporal evolution as well as global properties of the processes We evaluate our methods by simulating rumor and anti-rumor processes on a large data set of around 10^5 nodes derived from the social networking site Twitter and on a synthetic network of the same size generated according to the Barab\'asi-Albert model

Towards combating rumors in social networks: Models and metrics

In this article we describe a visual-analytic tool for the interrogation of evolving interaction network data such as those found in social, bibliometric, WWW and biological applications. The tool we have developed incorporates common visualization paradigms such as zooming, coarsening and filtering while naturally integrating information extracted by a previously described event-driven framework for characterizing the evolution of such networks. The visual front-end provides features that are specifically useful in the analysis of interaction networks, capturing the dynamic nature of both individual entities as well as interactions among them. The tool provides the user with the option of selecting multiple views, designed to capture different aspects of the evolving graph from the perspective of a node, a community or a subset of nodes of interest. Standard visual templates and cues are used to highlight critical changes that have occurred during the evolution of the network. A key challenge we address in this work is that of scalability - handling large graphs both in terms of the efficiency of the back-end, and in terms of the efficiency of the visual layout and rendering. Two case studies based on bibliometric and Wikipedia data are presented to demonstrate the utility of the toolkit for visual knowledge discovery.

A visual-analytic toolkit for dynamic interaction graphs

Periodicity detection is an important pre-processing step for many time series algorithms. It provides important information about the structural properties of a time series. Feature vectors based on periodicity can be used for clustering, classification, abnormality detection, and human motion understanding. The periodicity detection task is not difficult in case of simple and uncontaminated signal. Unfortunately, most of the real datasets exhibit one or more of the following properties: i) non-stationarity, ii) interlaced cyclic patterns and iii) data contamination, which makes the period detection extremely challenging. A seemingly straightforward solution is to develop individual specialized algorithms for handling each case separately. However, determining if a time series is non-stationary or is contaminated in itself is an extremely difficult task. In this article, we propose generic algorithms which can detect periods in complex, noisy and incomplete datasets. The algorithm leverages the frequency characterization and autocorrelation structure inherent in a time series to estimate its periodicity. We extend the methods to handle non-stationary time series by tracking the candidate periods using a Kalman filter. We also address the interesting problem of finding multiple interlaced periodicities.

Sameep Mehta

Papers

Method, system and computer program product for server selection, application placement and consolidation

Toward unsupervised correlation preserving discretization

Towards combating rumors in social networks: Models and metrics

A visual-analytic toolkit for dynamic interaction graphs

Robust periodicity detection algorithms