Spammers Detection on Twitter by Automated Multi Level Detection System

Twitter is one of the most well known micro-blogging administrations, which is commonly used to share news and updates through short messages confined to 280 characters. In any case, its open nature and huge client base are every now and again misused via robotized spammers, content polluters, and other not well expected clients to carry out different cyber violations, for example, cyber bullying, trolling, rumor dissemination, and stalking. Likewise, various methodologies have been proposed by specialists to address these issues. Nonetheless, the majority of these methodologies depend on client portrayal and totally dismissing shared communications. In this examination, we present a hybrid methodology for recognizing mechanized spammers by amalgamating network based features with other feature classifications, to be specific metadata-, content-, and association based features. The curiosity of the proposed methodology lies in the portrayal of clients dependent on their communications with their supporters given that a client can dodge features that are identified with his/her very own exercises, yet sidestepping those dependent on the devotees is troublesome. Nineteen distinct features, including six recently characterized features and two re-imagined features, are distinguished for learning three classifiers, in particular, irregular woods, choice tree, Bayesian system, and example pre-handling on a genuine dataset that involves generous clients and spammers. The separation intensity of various feature classifications is additionally broke down, and cooperation and network based features are resolved to be the best for spam identification, though metadata-based features are demonstrated to be the least compelling

Twitter Spam Detection Using Hybrid Method

We present a half breed approach for perceiving automated spammers by amalgamating system based features with other component classes, to be explicit metadata-, content-, and affiliation based features. The peculiarity of the proposed approach lies in the portrayal of customers subject to their relationship with their disciples given that a customer can evade incorporates that are related to his/her very own activities, anyway avoiding those reliant on the enthusiasts is problematic. Nineteen special features, including six as of late portrayed features and two renamed features, are perceived for learning three classifiers, specifically, self-assertive boondocks, decision tree, and Bayesian framework, on a veritable dataset that contains charitable customers and spammers. The partition force of different component classes is furthermore analyzed, and association and system based features are made plans to be the best for spam ID, however metadata-based highlights are demonstrated to be the least powerful

Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate

Terror attacks have been linked in part to online extremist content. Although tens of thousands of Islamist extremism supporters consume such content, they are a small fraction relative to peaceful Muslims. The efforts to contain the ever-evolving extremism on social media platforms have remained inadequate and mostly ineffective. Divergent extremist and mainstream contexts challenge machine interpretation, with a particular threat to the precision of classification algorithms. Our context-aware computational approach to the analysis of extremist content on Twitter breaks down this persuasion process into building blocks that acknowledge inherent ambiguity and sparsity that likely challenge both manual and automated classification. We model this process using a combination of three contextual dimensions -- religion, ideology, and hate -- each elucidating a degree of radicalization and highlighting independent features to render them computationally accessible. We utilize domain-specific knowledge resources for each of these contextual dimensions such as Qur'an for religion, the books of extremist ideologues and preachers for political ideology and a social media hate speech corpus for hate. Our study makes three contributions to reliable analysis: (i) Development of a computational approach rooted in the contextual dimensions of religion, ideology, and hate that reflects strategies employed by online Islamist extremist groups, (ii) An in-depth analysis of relevant tweet datasets with respect to these dimensions to exclude likely mislabeled users, and (iii) A framework for understanding online radicalization as a process to assist counter-programming. Given the potentially significant social impact, we evaluate the performance of our algorithms to minimize mislabeling, where our approach outperforms a competitive baseline by 10.2% in precision.Comment: 22 page

Comparando a eficácia na recuperação de questionários: QSMatching vs Vector model vs Fuzzy

Elaborar um questionário útil representa uma tarefa importante para a pesquisa descritiva. Perguntas mal elaboradas podem levar a respostas com interpretações sem sentido, sutis ou ingênuas. Portanto, pode ser interessante reutilizar, parcial ou totalmente, questionários já criados com o mesmo propósito. Neste trabalho, comparamos o QSMatching com os modelos vetorial e fuzzy para calcular a similaridade entre questionários e, consequentemente, obter uma ordenação de questionários de acordo com a consulta do usuário. Para verificar a efetividade, foi realizado um experimento comparando as abordagens QSMatching, modelo vetorial e fuzzy. O resultado da análise do experimento mostra que o QSMatching é mais efetivo que outros modelos para recuperação de questionários

Ranking radically influential web forum users

The growing popularity of online social media is leading to its widespread use among the online community for various purposes. In the recent past, it has been found that the web is also being used as a tool by radical or extremist groups and users to practice several kinds of mischievous acts with concealed agendas and promote ideologies in a sophisticated manner. Some of the web forums are predominantly being used for open discussions on critical issues influenced by radical thoughts. The influential users dominate and influence the newly joined innocent users through their radical thoughts. This paper presents an application of collocation theory to identify radically influential users in web forums. The radicalness of a user is captured by a measure based on the degree of match of the commented posts with a threat list. Eleven different collocation metrics are formulated to identify the association among users, and they are finally embedded in a customized PageRank algorithm to generate a ranked list of radically influential users. The experiments are conducted on a standard data set provided for a challenge at ISI-KDD'12 workshop to find radical and infectious threads, members, postings, ideas, and ideologies. Experimental results show that our proposed method outperforms the existing UserRank algorithm. We also found that the collocation theory is more effective to deal with such ranking problem than the textual and temporal similarity-based measures studied earlier