A Network Topology Approach to Bot Classification

Automated social agents, or bots, are increasingly becoming a problem on social media platforms. There is a growing body of literature and multiple tools to aid in the detection of such agents on online social networking platforms. We propose that the social network topology of a user would be sufficient to determine whether the user is a automated agent or a human. To test this, we use a publicly available dataset containing users on Twitter labelled as either automated social agent or human. Using an unsupervised machine learning approach, we obtain a detection accuracy rate of 70%

Analysis of monotonicity properties of some rule interestingness measures

One of the crucial problems in the field of knowledge discovery is development of good interestingness measures for evaluation of the discovered patterns. In this paper, we consider quantitative, objective interestingness measures for "if..., then... " association rules. We focus on three popular interestingness measures, namely rule interest function of Piatetsky-Shapiro, gain measure of Fukuda et al., and dependency factor used by Pawlak. We verify whether they satisfy the valuable property M of monotonic dependency on the number of objects satisfying or not the premise or the conclusion of a rule, and property of hypothesis symmetry (HS). Moreover, analytically and through experiments we show an interesting relationship between those measures and two other commonly used measures of rule support and anti-support

Applications of opinion mining to data journalism

Dissertação de mest., Processamento de Linguagem Natural e Indústrias da Língua, Faculdade de Ciências Humanas e Sociais, Univ. do Algarve, 2013Nowadays social media play a central role in every day life. A huge volume of user-generated data spins around online social networks, such as Twitter, having an extraordinary impact on the media industry and on the users’ everyday life. More and more users and people use social networks from their computers and smartphones to share their emotions and opinions about the facts happening in the world. Natural language processing and, in particular, sentiment analysis are key technologies to make sense out of the data about news that circulates in the online social networks. The application of opinion mining to news-oriented user-generated contents, such as news-linking tweets, can provide novel views on the news audience behaviour and help to interpret the evolution of sentiments. Applying this capability in the social news-sphere permits (i) to measure the impact of news onto readers and (ii) to gather elements that contain stories. From a broad perspective, the main aim of this research is to face this challenge, that is, to explore how opinion mining (or sentiment analysis) can be adopted into the field of digital media and data-driven journalism

Weakly-supervised appraisal analysis

This article is concerned with the computational treatment of Appraisal, a Systemic Functional Linguistic theory of the types of language employed to communicate opinion in English. The theory considers aspects such as Attitude (how writers communicate their point of view), Engagement (how writers align themselves with respect to the opinions of others) and Graduation (how writers amplify or diminish their attitudes and engagements). To analyse text according to the theory we employ a weakly-supervised approach to text classification, which involves comparing the similarity of words with prototypical examples of classes. We evaluate the method's performance using a collection of book reviews annotated according to the Appraisal theory

Multi-Target Prediction: A Unifying View on Problems and Methods

Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research

Hybrid Recommender Systems: A Systematic Literature Review

Recommender systems are software tools used to generate and provide suggestions for items and other entities to the users by exploiting various strategies. Hybrid recommender systems combine two or more recommendation strategies in different ways to benefit from their complementary advantages. This systematic literature review presents the state of the art in hybrid recommender systems of the last decade. It is the first quantitative review work completely focused in hybrid recommenders. We address the most relevant problems considered and present the associated data mining and recommendation techniques used to overcome them. We also explore the hybridization classes each hybrid recommender belongs to, the application domains, the evaluation process and proposed future research directions. Based on our findings, most of the studies combine collaborative filtering with another technique often in a weighted way. Also cold-start and data sparsity are the two traditional and top problems being addressed in 23 and 22 studies each, while movies and movie datasets are still widely used by most of the authors. As most of the studies are evaluated by comparisons with similar methods using accuracy metrics, providing more credible and user oriented evaluations remains a typical challenge. Besides this, newer challenges were also identified such as responding to the variation of user context, evolving user tastes or providing cross-domain recommendations. Being a hot topic, hybrid recommenders represent a good basis with which to respond accordingly by exploring newer opportunities such as contextualizing recommendations, involving parallel hybrid algorithms, processing larger datasets, etc