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Taxonomies are frequently used to represent knowledge; a taxonomy is a set of concepts partially ordered by a subsumption (is-a) relation. Recently, the CEDAR project has proposed a CEDAR Semantic Web reasoner based on OSF logic, which has shown very promising results with respect to both efficiency and scalability. Indeed, the basic operations behind the reasoning mechanism of CEDAR amount to answering Boolean queries on a taxonomy. For instance, CEDAR answers a conjunctive query on a taxonomy by computing the greatest lower bound of a subset of its elements. This paper proposes a generalization of this operation to the setting where the taxonomy is regarded as a fuzzy partially ordered set and an answer to a conjunctive query is associated with a satisfaction (approximation) degree. A conjunctive query on the fuzzy taxonomy can be answered by applying the same encoding technique of the CEDAR reasoner, while the approximation degree of an answer can be computed by adapting shortest path algorithms for directed acyclic graphs that can be further optimized thanks to the information provided by the encoding.

Locating Web content useful to specific user needs and tasks concerns nowadays, in many circumstances, to assess the credibility of the content itself. With the diffusion of social media and the possibility for everyone to become a content generator, the problem of assessing information credibility has become a major research issue, in particular in microblogging sites, where fake news, hoaxes and other kinds of misinformation are diffused almost without any traditional form of trusted intermediation. In this paper, we propose an approach based on multiple criteria associated with news, on which the use of aggregation operators guided by linguistic quantifiers allow the modeling of the decision maker behavior into the news credibility assessment process. The operation and the evaluation of the approach are illustrated by considering the Twitter microblogging platform.

The Social Web represents nowadays the principal means to support and foster social interactions among people through Web 2.0 technologies. Individuals interact in virtual communities to pursue mutual interests or goals, by exchanging multiple kinds of contents (i.e., textual, acoustic, visual), the so-called User-Generated Content (UGC). In this context, the SeCredISData Special Session is especially devoted at discussing the implications that the analysis of big social data has in tackling open issues related to society from different perspectives. On one side, there is the need to push forward the research on emotion and sentiment, and the investigation of affective cognitive models and their possible integration into intelligent systems. On the other side, it is urgent to address the issue of on-line information credibility assessment, in an era where trusted intermediaries have disappeared and people must rely only on their cognitive capacities to judge information. The Special Session is therefore aimed at promoting the development of models and applications able to tackle these issues

Latent Dirichlet Allocation (LDA) has become the most stable and widely used topic model to derive topics from collections of documents where it depicts different levels of success based on diversified domains of inputs. Nevertheless, it is a vital requirement to evaluate the LDA against the quality of the input. The noise and uncertainty of the content create a negative influence on the topic model. The major contribution of this investigation is to critically evaluate the LDA based on the quality of input sources and human perception. The empirical study shows the relationship between the quality of the input and the accuracy of the output generated by LDA. Perplexity and coherence have been evaluated with three data-sets (RCV1, conference data set, tweets) which contain different level of complexities and uncertainty in their contents. Human perception in generating topics has been compared with the LDA in terms of human defined topics. Results of the analysis demonstrate a strong relationship between the quality of the input and generated topics. Thus, highly relevant topics were generated from formally written contents while noisy and messy contents lead to generate meaningless topics. A considerable gap is noticed between human defined topics and LDA generated topics. Finally, a concept-based topic modeling technique is proposed to improve the quality of topics by capturing the meaning of the content and eliminating the irrelevant and meaningless topics.

Topic Modelling has been applied in many successful applications in data mining, text mining, machine learning and information filtering. The limitation is that the quality of topics generated from modelled corpus are not always good because many topics contain intrusive and ambiguous words. This negative drawback would affect the performance of text based application systems based on topic models. Hence, topic evaluation to assess and to rank the topics is really important for the good quality topics before applying those topics to text based applications. In this study, we proposed an ontology-based topic evaluation method for enhancing information filtering, named as STRbTCM. This new model assesses the quality of topics by matching topic models with headings in Library Congress Subject Heading (LCSH) ontology. To evaluate the effectiveness of our proposed model, we compare the model with two existing topic evaluation methods applied to information filtering system. In addition, we also compare our proposed model to term-based model BM25 and two other models based on topics: TNG and LDA_words. Through extensive experiments, we find that our proposed model performed better than other baseline models according to four main evaluating measures.

User preference discovery aims to detect the patterns of user preferences for various topics of interest or items such as movie genre or category. Preferences discovery is a crucial stage in the development of intelligent personalization systems. Although a variety of studies have been proposed in the literature addressing a wide range of applications such as recommender systems or personalized search, only a few of them have considered the management of imprecision in the representation of user and item features. This paper aims to address the above issue by using fuzzy sets. The paper proposes a general framework for preferences discovery through fuzzy sets and fuzzy models and it introduces a new algorithm for representing and discovering fuzzy user interest profile. Based on the results of the empirical evaluation, the proposed approach outperforms two well-known recommendation approaches in terms of well-known quality assessment metrics, namely: discounted cumulative gain, precision, recall, as well as F1-measure.

Nowadays, review sites are more and more confronted with the spread of misinformation, i.e., opinion spam, which aims at promoting or damaging some target businesses, by misleading either human readers, or automated opinion mining and sentiment analysis systems. For this reason, in the last years, several data-driven approaches have been proposed to assess the credibility of user-generated content diffused through social media in the form of on-line reviews. Distinct approaches often consider different subsets of characteristics, i.e., features, connected to both reviews and reviewers, as well as to the network structure linking distinct entities on the review-site in exam. This article aims at providing an analysis of the main review- and reviewer-centric features that have been proposed up to now in the literature to detect fake reviews, in particular from those approaches that employ supervised machine learning techniques. These solutions provide in general better results with respect to purely unsupervised approaches, which are often based on graph-based methods that consider relational ties in review sites. Furthermore, this work proposes and evaluates some additional new features that can be suitable to classify genuine and fake reviews. For this purpose, a supervised classifier based on Random Forests have been implemented, by considering both well-known and new features, and a large-scale labeled dataset from which all these features have been extracted. The good results obtained show the effectiveness of new features to detect in particular singleton fake reviews, and in general the utility of this study.

News blogs provide valuable insights into various news events and serve as opinion pieces with respect to the different happenings around the world. This demonstration paper presents a visual analytics system for these blogs, the system aims to facilitate news consumers by clustering the various blogposts along different topical dimensions. Moreover, the predictive module within the system provides an indication of how likely it is for topics in a particular opinion piece to resurface on the blog section of the particular news website and this helps in identification of the crucial stories worth keeping an eye on.

Despite the usage of Facebook data by researchers for various types of personality analyses there has been no investigation into the "comment-liking" feature of Facebook. Two studies are performed which analyse the link between narcissism and users' interactions on Facebook. Firstly, comment-liking activity is measured and this is compared to narcissism scores. Secondly, measures of promotional self-worth derived from the content analysis of users' posts are also compared to narcissism scores. The results of the comparisons show that narcissistic behaviour is observable in the tendency of Facebook users to selectively respond to their Facebook friends' comments while at the same time indulging in conversations lacking diversity and for the most part focusing on self-promotion.

Many websites presently provide the facility for users to rate items quality based on user opinion. These ratings are used later to produce item reputation scores. The majority of websites apply the mean method to aggregate user ratings. This method is very simple and is not considered as an accurate aggregator. Many methods have been proposed to make aggregators produce more accurate reputation scores. In the majority of proposed methods the authors use extra information about the rating providers or about the context (e.g. time) in which the rating was given. However, this information is not available all the time. In such cases these methods produce reputation scores using the mean method or other alternative simple methods. In this paper, we propose a novel reputation model that generates more accurate item reputation scores based on collected ratings only. Our proposed model embeds statistical data, previously disregarded, of a given rating dataset in order to enhance the accuracy of the generated reputation scores. In more detail, we use the Beta distribution to produce weights for ratings and aggregate ratings using the weighted mean method. Experiments show that the proposed model exhibits performance superior to that of current state-of-the-art models.

Among the existing global optimization algorithms, Particle Swarm Optimization (PSO) is one of the most effective when dealing with non-linear and complex high-dimensional problems. However, the performance of PSO is strongly dependent on the choice of its settings. In this work we propose a novel and self-tuning PSO algorithm - called Proactive Particles in Swarm Optimization (PPSO) - which exploits Fuzzy Logic to calculate the best setting for the inertia, cognitive factor and social factor. Thanks to additional heuristics, PPSO automatically determines also the best setting for the swarm size and for the particles maximum velocity. PPSO significantly differs from other versions of PSO that exploit Fuzzy Logic, since specific settings are assigned to each particle according to its history, instead of being globally defined for the whole swarm. Thus, the novelty of PPSO is that particles gain a limited autonomous and proactive intelligence, instead of being simple reactive agents. Our results show that PPSO outperforms the standard PSO, both in terms of convergence speed and average quality of solutions, remarkably without the need for any user setting.

The Internet of Things and the Web of Things have focused on context awareness as a central issue in defining complex autonomic systems that rely on various layers, including devices, communications and applications. The spread usage of mobile devices that allow users to generate and to access distributed data makes it extremely important to organize the forwarding and gathering of such data in a user centered way, which may support a targeted process of data analytics. Data science should strongly take into account the individual and her/his needs to look at data from a personal perspective. In this contribution an integrated view of context awareness is presented, with the several technological and scientific issues that it raises.

The social tags in web 2.0 are becoming another important information source to profile users' interests and preferences to make personalized recommendations. To solve the problem of low information sharing caused by the free-style vocabulary of tags and the long tails of the distribution of tags and items, this paper proposes an approach to integrate the social tags given by users and the item taxonomy with standard vocabulary and hierarchical structure provided by experts to make personalized recommendations. The experimental results show that the proposed approach can effectively improve the information sharing and recommendation accuracy.

Recommender Systems is one of the effective tools to deal with information overload issue. Similar with the explicit rating and other implicit rating behaviours such as purchase behaviour, click streams, and browsing history etc., the tagging information implies user’s important personal interests and preferences information, which can be used to recommend personalized items to users. This paper is to explore how to utilize tagging information to do personalized recommendations. Based on the distinctive three dimensional relationships among users, tags and items, a new user profiling and similarity measure method is proposed. The experiments suggest that the proposed approach is better than the traditional collaborative filtering recommender systems using only rating data.

The recent emergence of location-based social networking services is revolutionizing web-based social networking allowing users to share real-life experiences via geo-tagged user-generated multimedia content. One of the key challenges of the web-based social networks as an information sharing and exchanging channel is how to manage healthy relationships among community users and ensure the quality of the information shared and exchanged within the community, which holds a very significant importance to user satisfaction. Deciding whom and what information to trust is very difficult in environment where the users are unknown to each other. This paper investigates the possibilities of managing trust between the users of a web-based social network while recommending items to the members of the network. A novel framework is proposed to integrate trust among community members and public reputation of items to recommend the most appropriate items to a user of the network.

Semantic relatedness is an important part of developing ontology based systems, as it provides the ability to measure the relatedness of two concepts. This is important not only for computational linguistics but also for intelligent querying and data classification. In this paper we present a rule-based calculation for a semantic relatedness score. This metric was specifically developed for Loculus, an ontology we have developed for the Motion Picture Industry. The Metric is evaluated using three axes: the Temporal Axis, the Inheritance Axis and the Linkage Axis. The metric has been tested on pairs of concept; the resulting scores are consistent with human expectations.