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Recently, location features have become available by the most popular online social networks such as Facebook, Twitter, and Foursquare. These networks are called location-based social networks (LBSN), which allow users to share their locations and location-related content. One of the services that LBSNs present is friend recommendation. This service recommends new friends to users based on their posts, media, opinions, locations or social ties. Several studies have been conducted in the area of friend recommendation by LBSNs. Nevertheless, addressing users' opinions on different topics, and considering asymmetric (directed) relations as one indirect relation, are factors that have not been extensively reviewed. In this paper, we propose a new recommendation model in LBSNs for recommending friends to users based on social, spatial, and textual features. These features are extracted based on factors to enhance the accuracy of friend recommendation process. We propose a novel factor for the extraction of the textual feature, also, we enhance factors for social and spatial features. The recommendation model was tested and evaluated using two real datasets from Twitter. The results of recommending top-k friends showed an average accuracy of 97.88%, and the experiments also showed the efficiency of the recommendation model against another friend recommendation model.

In this paper we study the problem of finding semantic relationships between folksonomy tags. We investigate different methods used to embed tags in the vector space and find similarities between them using word embedding vectors. We also present two new methods for embedding tags in the vector space utilizing labeled Latent Dirichlet Allocation (LDA) and Wikipedia category links. Related tags are grouped into communities using an overlapping community detection technique. In order to evaluate tag embedding methods, we use three different evaluation metrics, two of them do not require a ground truth dataset and the third is based on a manually created dataset of ground truth communities. Our results show that representing folksonomy tags using bag of words and embedding this representation in the vector space yields the best results compared to embedding co-occurring tags only or embedding tags along with textual content of tagged documents. We also compare between using word embedding, Latent Semantic Indexing (LSI), and LDA to find similarities between bag of words representations of tags. We show that word embedding outperforms LSI in one representation, while LDA is hard to beat.

In the present generation, on-Line social networks (OSNs) have become increasingly popular, people’s social lives have become more associated with these sites. They use on-Line social networks (OSNs) to keep in touch with each others, share news, organize events, and even run their own e-business. The rapid growth of OSNs and the massive amount of personal data of its subscribers have attracted attackers, and imposters to steal personal data, share false news, and spread malicious activities. On the other hand, researchers have started to investigate efficient techniques to detect abnormal activities and fake accounts relying on accounts features, and classification algorithms. However, some of the account’s exploited features have negative contribution in the final results or have no impact, also using standalone classification algorithms does not always achieve satisfactory results. In this paper, a new algorithm, SVM-NN, is proposed to provide efficient detection for fake Twitter accounts and bots, feature selection and dimension reduction techniques were applied. Machine learning classification algorithms were used to decide the target accounts identity real or fake, those algorithms were support vector machine (SVM), neural Network (NN), and our newly developed algorithm, SVM-NN. The proposed algorithm (SVM-NN) uses less number of features, while still being able to correctly classify about 98% of the accounts of our training dataset.

Graph database management systems are widely used in scenarios where the data are intensively connected. Handling such connected data in a relational database is not an efficient task. Converting relational databases to graph ones is one of the solutions that can empower users with handling such data using the graph model features. In this paper, we propose a new algorithm to ease such conversion and overcome the limitations of the existing algorithms. The state of the art algorithms cannot handle multiple relationships types such as unary relationships and associative entities with non-foreign key attributes. Our proposed algorithm, FD2G, leverages the existence of functional dependencies information inside the input relational database to automatically perform the conversion to property graph databases. In addition, we updated the state of the art algorithm, named R2G, to handle its limitations and be able to fairly compare both algorithms performance. We evaluated FD2G against the updated R2G algorithm where it efficiently and effectively outperformed the existing one.

Online Communities for Question Answering (CQA) such as Quora and Stack Overflow face the challenge of providing sufficient answers for the questions asked by users. The exponential growing rate of the unanswered questions compromises the effectiveness of the CQA frameworks as knowledge sharing platforms. The main reason for this issue is the inefficient routing of the questions to the potential answerers, who are the field experts and interested users. This paper proposes the deep-learning-based technique QR-DSSM to increase the accuracy of the question routing process. This technique uses deep semantic similarity model (DSSM) to extract semantic similarity features using deep neural networks and use the features to rank users' profiles. QR-DSSM maps the asked questions and the profiles of the users into a latent semantic space where the ability to answer is measured using the cosine similarity between the questions and the profiles of the users. QR-DSSM experiments outperformed the baseline models such as LDA, SVM, and Rank-SVM techniques and achieved an MRR score of 0.1737.

The use of social media platforms such as social networks, weblogs and community question answering websites increased largely in the last few years. This increase in usage contributed to the vast explosion in available online content. Some of these platforms use social tagging, tags manually inserted by content authors, as a way to facilitate content description and discovery. In this paper, our goal is to automatically group semantically related tags in order to organize the large amount of tags contributed by various users. Our approach is based on using a topic model to discover topics of documents, and then grouping top tags related to documents assigned to each topic. We perform a set of experiments using different number of topics to provide different levels of details for the generated tag groups. The dataset used in our experiments is extracted from Stack Overflow, a community question answering website used by programming professionals. Tag groups generated by our technique are presented and evaluated.

The vast amount of text published daily over the internet pose an opportunity to build unsupervised text mining models with a better or a comparable performance than existing models. In this paper, we investigate the problem of relation extraction and generation from text using an unsupervised model learned from news published online. We propose a clustering-based method to build a dataset of relations examples. News articles are clustered and once a cluster of sentences for each event in each piece of news is formed, relations between important entities in each event cluster are extracted and considered as examples of relations. Relations examples are used to build extraction templates in order to extract and generate readable relations summaries from new instances of news. The proposed unsupervised relation extraction and generation method is evaluated against multiple methods for relation extraction over different datasets where the proposed method has shown a comparable performance.

Online Communities for Question Answering (CQA) such as Quora and Stack Overflow face the challenge of providing high quality answers to the questions asked by their users. Although CQA frameworks receive new questions in a linear rate, the rate of the unanswered questions increases in an exponential way. This variation eventually compromise effectiveness of the CQA frameworks as knowledge sharing platforms. The main cause for this challenge is the improper routing of questions to the potential answerers, field experts or interested users. The proposed technique QR-DSSM uses deep semantic similarity model (DSSM) to extract semantic similarity features using deep neural networks. The extracted semantic features are used to rank the profiles of the answerers by their relevance the routed question. QR-DSSM maps the asked questions and the profiles of the users into a latent semantic space where the relevance is measured using cosine similarity between the two; questions and users’ profiles. QR-DSSM achieved MRR score of 0.1737. QR-DSSM outperformed the baseline models such as query likelihood language model (QLLM), Latent Dirichlet Allocation (LDA), SVM classification technique and RankingSVM learning to rank technique.

A considerable amount of attributes in real datasets are not numerical, but rather textual and categorical. We investigate the problem of identifying outliers in categorical and textual datasets. We propose a clustering-based semi-supervised outlier detection method which basically represents normal and unlabeled data points as a bipartite graph. We leverage the existing free of parameters clustering techniques to cluster the resulting graph. The bipartite graph is clustered with a specific end goal to distinguish unlabeled data points as either outliers or normal data points. The proposed method is evaluated using multiple categorical and textual datasets against one-class support vector machines classifier and FRaC approach for semi-supervised outlier detection where the proposed method has shown a comparable performance.

Opinions are considered a powerful source of market research. Social networks are the most popular mean for people to state their opinions about products and services. Companies invest in analysing their customers' opinions from multiple existing social media platforms. The knowledge extracted from social media contains sentiment data that is not included in corporate database. This extracted data can be used to improve marketing campaigns to better retain customers and meet their needs. The integration between both social media data and corporate data can lead to better insights that would not have been possible to gain without such integration. This paper proposes a framework to integrate sentiment data, extracted from customers' opinions with corporate data of an organization. The process of integrating opinions streams into corporate database is proposed, then a multidimensional data warehouse is built over the integrated database to perform advanced analytical tasks and answer queries that would not have been possible without the integration. The new framework has been experimented with a case study of integrating opinions about digital devices and corporate data to illustrate the usage of the integrated model.