A relevance index-based method for improved detection of malicious users in social networks

The phenomenon of “trolling” in social networks is becoming a very serious threat to the online presence of people and companies, since it may affect ordinary people, public profiles of brands, as well as popular characters. In this paper, we present a novel method to preprocess the temporal data describing the activity of possible troll profiles on Twitter, with the aim of improving automatic troll detection. The method is based on the zI, a Relevance Index metric usually employed in the identification of relevant variable subsets in complex systems. In this case, the zI is used to group different user profiles, detecting different behavioral patterns for standard users and trolls. The comparison of the results, obtained on data preprocessed using this novel method and on the original dataset, demonstrates that the technique generally improves the classification performance of troll detection, virtually independently of the classifier that is used

Application of Lovheim model for emotion detection in english tweets

Emotions are central for a wide range of everyday human experiences and understanding emotions is a key problem both in the business world and in the fields of physiology and neuroscience. The most well-known theory of emotions proposes a categorical systemof emotion classification, where emotions are classified as discrete entities, while psychologists say that in general man will hardly express a single basic emotion. According to this observation, alternative models have been developed, which define multiple dimensions corresponding to various parameters and specify emotions along those dimensions. Recently, one of the most used models in affective computing is the Lovheim’s cube of emotions, i.e., a theoretical model that focuses on the interactions of monoamine neurotransmitters and emotions. This work presents a comparison between a single automatic classifier able to recognize the basic emotions proposed in the Lovheim’s cube and a set of independent binary classifiers, each one able to recognize a single dimension of the Lovehim’s cube. The application of this model has determined a notable improvement of results: in fact, in the best case there is an increment of the accuracy of 11,8%. The set of classifiers has been modeled and deployed on the distributed ActoDeS application architecture. This implementation improves the computational performance and it eases the system reconfiguration and its ability to recognize particular situations, consisting of particular combinations of basic emotions

Automatic processing and classification of citizens’ reports

We present a comprehensive framework for managing reports sent to the local government by citizens through wellknown instant messaging apps. It leverages a combined use of Web systems and automated bots, based on Machine Learning techniques. This project has been developed in collaboration with the local administration of Montecchio Emilia (Italy). The results show that an automatic classification system of this kind can reach very good levels of accuracy, also above 90%

Image-based hoax detection

In the last few years, the impact of information spread through online social networks has continuously grown. For this reason, understanding the trustworthiness of news has become one of the most important challenges for an Internet user, especially during crisis events or in political, health and social issues. As part of a more comprehensive project for the detection of fake news, this paper proposes a machine learning method to evaluate the trustworthiness of a piece of information especially considering its associated image. In the work described in this paper, the training and test datasets have been first collected from the web, downloading more than 1000 images related to trusted and fake Facebook pages. All collected images have been processed using the Google Vision online service for extracting their specific internal details. For each image, various kinds of features have been considered, including its color composition, the recognized objects, the list of sites in which it is published, and eventually the contained text. These details have been then used for training a classifier using different algorithms which allowed us to reach an accuracy of about 85% in hoax identification. Future research will focus on social-network information related to images, to improve the system accuracy and acquire more knowledge about various types of news spread online

Improving sentiment analysis using preprocessing techniques and lexical patterns

Sentiment analysis has recently gained considerable attention, since the classification of the emotional content of a text (online reviews, blog messages etc.) may have a relevant impact on market research, political science and many other fields. In this paper, we focus on the importance of the text preprocessing phase, proposing a new technique we termed lexical pattern-based feature weighting (LPFW) that allows one to improve sentence-level sentiment analysis by increasing the relevance of the features contained in particular lexical patterns. This approach has been evaluated on two sentiment classification datasets. We show that a systematic optimisation of the preprocessing filters is important for obtaining good classification accuracy. Also, we show that LPFW is effective in different application domains and with different training set sizes

Guess the movie - Linking facebook pages to IMDb movies

In Facebook, the set of pages liked by some users represents an important knowledge about their real life tastes. However, the process of classification, which is already hard when dealing with dozens of classes and genres, is made even more difficult by the very coarse information of Facebook pages. Our work originates from a large dataset of pages liked by users of a Facebook app. To overcome the limitations of multilabel automatic classification of free-form user-generated pages, we acquire data also from IMDb, a large public database about movies. We use it to associate with high accuracy a given cinema-related page on Facebook to the corresponding record on IMDb, which includes plenty of metadata in addition to genres. To this aim, we compare different approaches. The obtained results demonstrate that the highest accuracy is obtained by the combined use of different methods and metrics

A comparison between preprocessing techniques for sentiment analysis in Twitter

In recent years, Sentiment Analysis has become one of the most interesting topics in AI research due to its promising commercial benefits. An important step in a Sentiment Analysis system for text mining is the preprocessing phase, but it is often underestimated and not extensively covered in literature. In this work, our aim is to highlight the importance of preprocessing techniques and show how they can improve system accuracy. In particular, some different preprocessing methods are presented and the accuracy of each of them is compared with the others. The purpose of this comparison is to evaluate which techniques are effective. In this paper, we also present the reasons why the accuracy improves, by means of a precise analysis of each method

Cold seeps along the main Marmara Fault in the Sea of Marmara (Turkey)

The main Marmara Fault exhibits numerous sites of fluid venting, observed during previous cruises and in particular with R.O.V. VICTOR during the MARMARASCARPS cruise (2002). Long CALYPSO cores were recovered near active vents and at reference sites during the MARMARA-VT cruise (2004), together with echosounder sub-bottom profiles (frequency of 3.5kHz). We compiled R.O.V. video observations from MARMARASCARPS cruise and show that all known seeps occur in relationship with strike-slip faults, providing pathways for fluid migration. Among the main active sites, a distinction is made between gas seeps and water seeps. At gas seeps, bubble emissions at the seafloor or disturbed echofacies on sounder profiles demonstrate the presence of free methane gas at a shallow depth within the sediment. Most cores displayed gas-related expansion, most intense for cores taken within the gas plumes. On the other hand. authigenic carbonate chimneys characterize the water seeps and visible water outflow was observed at two sites (in the Tekirdag and Central basins). The pore fluid chemistry data show that the water expelled at these sites is brackish water trapped in the sediment during lacustrine times (before 14 cal kyr BP), in relation with the paleoceanography in the Sea of Marmara. The chimney site in the Tekirdag Basin is located at the outlet of a canyon feeding a buried fan with coarse sandy turbidites. Pore fluid composition profiles indicate that the sand layers channel the brackish fluids laterally from the basin into the fault zone at less than 20 m depth. However, a deeper gas source cannot be excluded. (c) 2008 Elsevier Ltd. All rights reserved