Social Network Analysis

The widespread prevalence and persistence of misinformation in contemporary societies, such as the false belief that there is a link between childhood vaccinations and autism, is a matter of public concern. For example, the myths surrounding vaccinations, which prompted some parents to withhold immunization from their children, have led to a marked increase in vaccine-preventable disease, as well as unnecessary public expenditure on research and public-information campaigns aimed at rectifying the situation.We first examine the mechanisms by which such misinformation is disseminated in society, both inadvertently and purposely. Misinformation can originate from rumors but also from works of fiction, governments and politicians, and vested interests. Moreover, changes in the media landscape, including the arrival of the Internet, have fundamentally influenced the ways in which information is communicated and misinformation is spread.We next move to misinformation at the level of the individual, and review ...

/pdf/misinformation-and-its-correction-continued-influence-and-1r5m0vnn49.pdf

Misinformation and Its Correction: Continued Influence and Successful Debiasing

Recommender systems have the effect of guiding users in a personal- ized way to interesting objects in a large space of possible options. Content-based recommendation systems try to recommend items similar to those a given user has liked in the past. Indeed, the basic process performed by a content-based recom- mender consists in matching up the attributes of a user profile in which preferences and interests are stored, with the attributes of a content object (item), in order to recommend to the user new interesting items. This chapter provides an overview of content-based recommender systems, with the aim of imposing a degree of order on the diversity of the different aspects involved in their design and implementation. The first part of the chapter presents the basic concepts and terminology of content- based recommender systems, a high level architecture, and their main advantages and drawbacks. The second part of the chapter provides a review of the state of the art of systems adopted in several application domains, by thoroughly describ- ing both classical and advanced techniques for representing items and user profiles. The most widely adopted techniques for learning user profiles are also presented. The last part of the chapter discusses trends and future research which might lead towards the next generation of systems, by describing the role of User Generated Content as a way for taking into account evolving vocabularies, and the challenge of feeding users with serendipitous recommendations, that is to say surprisingly interesting items that they might not have otherwise discovered.

/pdf/content-based-recommender-systems-state-of-the-art-and-4luq4392k8.pdf

Content-based Recommender Systems: State of the Art and Trends

Frequent pattern mining has been a focused theme in data mining research for over a decade. Abundant literature has been dedicated to this research and tremendous progress has been made, ranging from efficient and scalable algorithms for frequent itemset mining in transaction databases to numerous research frontiers, such as sequential pattern mining, structured pattern mining, correlation mining, associative classification, and frequent pattern-based clustering, as well as their broad applications. In this article, we provide a brief overview of the current status of frequent pattern mining and discuss a few promising research directions. We believe that frequent pattern mining research has substantially broadened the scope of data analysis and will have deep impact on data mining methodologies and applications in the long run. However, there are still some challenging research issues that need to be solved before frequent pattern mining can claim a cornerstone approach in data mining applications.

/pdf/frequent-pattern-mining-current-status-and-future-directions-31jxo4xdkd.pdf

Frequent pattern mining: current status and future directions

With the advent of Web 2.0, people became more eager to express and share their opinions on web regarding day-to-day activities and global issues as well. Evolution of social media has also contributed immensely to these activities, thereby providing us a transparent platform to share views across the world. These electronic Word of Mouth (eWOM) statements expressed on the web are much prevalent in business and service industry to enable customer to share his/her point of view. In the last one and half decades, research communities, academia, public and service industries are working rigorously on sentiment analysis, also known as, opinion mining, to extract and analyze public mood and views. In this regard, this paper presents a rigorous survey on sentiment analysis, which portrays views presented by over one hundred articles published in the last decade regarding necessary tasks, approaches, and applications of sentiment analysis. Several sub-tasks need to be performed for sentiment analysis which in turn can be accomplished using various approaches and techniques. This survey covering published literature during 2002-2015, is organized on the basis of sub-tasks to be performed, machine learning and natural language processing techniques used and applications of sentiment analysis. The paper also presents open issues and along with a summary table of a hundred and sixty-one articles.

A survey on opinion mining and sentiment analysis

Smartphones are becoming a powerful platform for event recognition due to the number of sensors they are equipped with. This provides an opportunity to apply data mining techniques on movement data in order to recognize people's daily activities without changing their routine. In this paper, we present a methodology for collecting and analysing user activity information with a smartphone application. This information can be further exploited in various applications ranging from m-health (e.g. fitness application) and transportation (e.g. user driving habits detection) to m-commerce (e.g. shopping recommendations). In order to demonstrate this methodology, we have developed GPSTracker a prototype application for Android phones, which collects position, speed, altitude and time information and performs real-time classification of user's movement. The processed information is collected in a private user folder, on a cloud storage service, and can be further processed in order to extract aggregate user habits, or in order to detect user activity over time and provide recommendations. We also provide a visualisation of user trajectories, as recorded and classified by GPSTracker application. We exploited all the possible sensors of the smartphone and employed additional geo-location data from public transportation services. Finally, we tested several movement classification algorithms and trajectory pattern analysis techniques in order to improve the performance of our activity recognition process.

https://galaxy.hua.gr/~varlamis/Varlamis-papers/C63.pdf

Classification of movement data concerning user's activity recognition via mobile phones

The advancements in wearable technology, where embedded accelerometers, gyroscopes and other sensors enable the users to actively monitor their activity have made it easier for individuals to pursue a healthy lifestyle. However, most of the existing applications expect continuous feedback from the end users and fail to engage those who have busy schedules, or are not as committed and self-motivated. In this work, we propose a framework that employs machine learning and recommendation algorithms in order to smartly track and identify user’s activity by collecting accelerometer data, synchronizes with the user’s calendar, and recommends personalized workout sessions based on the user’s and similar users’ past activities, their preferences, as well as their physical state and availability. KEYWORDS—wearable technology, activity tracking, classification, recommendation, personalized assistant

/pdf/pro-fit-a-personalized-fitness-assistant-framework-44dzojmiey.pdf

PRO-Fit: A personalized fitness assistant framework.

Collaborative Filtering for Interactive Database Exploration

Popular social networking applications such as Facebook, Twitter, Friendster, etc. generate very large graphs with different characteristics. These social networks are huge, comprising millions of nodes and edges that push existing graph mining algorithms and architectures to their limits. In product-rating graphs, users connect with each other and rate items in tandem. In such bipartite graphs users and items are the nodes and ratings are the edges and collaborative filtering algorithms use the edge information (i.e. user ratings for items) in order to suggest items of potential interest to users. Existing algorithms can hardly scale up to the size of the entire graph and require unlimited resources to finish. This work employs a machine learning method for predicting the performance of Collaborative Filtering algorithms using the structural features of the bipartite graphs. Using a fast graph partitioning algorithm and information from the user-friendship graph, the original bipartite graph is partitioned into different schemes (i.e. sets of smaller bipartite graphs). The schemes are evaluated against the predicted performance of the Collaborative Filtering algorithm and the best partitioning scheme is employed for generating the recommendations. As a result, the Collaborative Filtering algorithms are applied to smaller bipartite graphs, using limited resources and allowing the problem to scale or be parallelized. Tests on a large, real-life, rating graph, show that the proposed method allows the collaborative filtering algorithms to run in parallel and complete using limited resources.

Scaling Collaborative Filtering to Large–Scale Bipartite Rating Graphs Using Lenskit and Spark

Crime has been prevalent in our society for a very long time and it continues to be so even today. Currently, many cities have released crime-related data as part of an open data initiative. Using this as input, we can apply analytics to be able to predict and hopefully prevent crime in the future. In this work, we applied big data analytics to the San Francisco crime dataset, as collected by the San Francisco Police Department and available through the Open Data initiative. The main focus is to perform an in-depth analysis of the major types of crimes that occurred in the city, observe the trend over the years, and determine how various attributes contribute to specific crimes. Furthermore, we leverage the results of the exploratory data analysis to inform the data preprocessing process, prior to training various machine learning models for crime type prediction. More specifically, the model predicts the type of crime that will occur in each district of the city. We observe that the provided dataset is highly imbalanced, thus metrics used in previous research focus mainly on the majority class, disregarding the performance of the classifiers in minority classes, and propose a methodology to improve this issue. The proposed model finds applications in resource allocation of law enforcement in a Smart City.

Magdalini Eirinaki

Papers

Classification of movement data concerning user's activity recognition via mobile phones

PRO-Fit: A personalized fitness assistant framework.

Collaborative Filtering for Interactive Database Exploration

Scaling Collaborative Filtering to Large–Scale Bipartite Rating Graphs Using Lenskit and Spark

Exploratory data analysis and crime prediction for smart cities