DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity

Nowadays, events usually burst and are propagated online through multiple modern media like social networks and search engines. There exists various research discussing the event dissemination trends on individual medium, while few studies focus on event popularity analysis from a cross-platform perspective. Challenges come from the vast diversity of events and media, limited access to aligned datasets across different media and a great deal of noise in the datasets. In this paper, we design DancingLines, an innovative scheme that captures and quantitatively analyzes event popularity between pairwise text media. It contains two models: TF-SW, a semantic-aware popularity quantification model, based on an integrated weight coefficient leveraging Word2Vec and TextRank; and wDTW-CD, a pairwise event popularity time series alignment model matching different event phases adapted from Dynamic Time Warping. We also propose three metrics to interpret event popularity trends between pairwise social platforms. Experimental results on eighteen real-world event datasets from an influential social network and a popular search engine validate the effectiveness and applicability of our scheme. DancingLines is demonstrated to possess broad application potentials for discovering the knowledge of various aspects related to events and different media

A reliability-based approach for influence maximization using the evidence theory

The influence maximization is the problem of finding a set of social network users, called influencers, that can trigger a large cascade of propagation. Influencers are very beneficial to make a marketing campaign goes viral through social networks for example. In this paper, we propose an influence measure that combines many influence indicators. Besides, we consider the reliability of each influence indicator and we present a distance-based process that allows to estimate the reliability of each indicator. The proposed measure is defined under the framework of the theory of belief functions. Furthermore, the reliability-based influence measure is used with an influence maximization model to select a set of users that are able to maximize the influence in the network. Finally, we present a set of experiments on a dataset collected from Twitter. These experiments show the performance of the proposed solution in detecting social influencers with good quality.Comment: 14 pages, 8 figures, DaWak 2017 conferenc

In Quest of Significance: Identifying Types of Twitter Sentiment Events that Predict Spikes in Sales

We study the power of Twitter events to predict consumer sales events by analysing sales for 75 companies from the retail sector and over 150 million tweets mentioning those companies along with their sentiment. We suggest an approach for events identification on Twitter extending existing methodologies of event study. We also propose a robust method for clustering Twitter events into different types based on their shape, which captures the varying dynamics of information propagation through the social network. We provide empirical evidence that through events differentiation based on their shape we can clearly identify types of Twitter events that have a more significant power to predict spikes in sales than the aggregated Twitter signal

Dynamics of trending topics between social media, news, and scientific literature

Information is disseminating more rapidly in today\u27s world than ever before in history. Every now and then, topics simultaneously gain massive attention in social media, dominate news headlines, and attract interest from researchers around the globe. While individual domains and networks are studied extensively, one question remains less addressed so far: How does information spread across different channels, considering dynamics between social media, news and, scientific literature? In this paper, we aim to identify frequent patterns in the dissemination of information over multiple channels. Based on an adapted pattern mining algorithm for multivariate time series, we provide strong indications for the existence of distinctive information diffusion effects between social media, news and scientific literature. We find that when all information channels simultaneously cover a certain topic, the preceding period is characterized either by a sole growth of social media coverage or a simultaneous growth of social media and news coverage

A data analysis approach to study events’ influence in social networks

Dissertação de mestrado em Computer ScienceNowadays, the assimilation of web content, by each individual, has a considerable impact on our’ everyday life. With the undeniable success of online social networks and microblogs, such as Facebook, Instagram and Twitter, the phenomenon of influence exerted by users of such platforms on other users, and how it propagates in the network, has been attracting, for some years computer scientists, information technicians, and marketing specialists. Increased connectivity, multi-model access and the rise of social media shortened the distance between almost every person in the world, more and more content is generated. Extracting and analyzing a significant amount of data is not a trivial task, Big Data techniques are essential. Through the analysis of this interaction, an exchange of information and feelings, it is entirely imaginable its usefulness in understanding complex human behaviours and so, help diverse organization’s decision-making. Influence maximization and viral marketing are among the possibilities. This work is intended to study what is the impact and role that an event’s social influence has and how does it propagate, particularly on its surrounding territory. This influence is inferred by analysis of the online platform’s data, by applying intelligent techniques, right after its extraction. The final step is to validate the results with data from different sources. Helping businesses through actionable and valuable knowledge is the ultimate goal. This document contemplates an introductory section where the study subject and its State of the Art are addressed. Next, the problem and what direction to take to solve it are discussed.Atualmente, a assimilação de conteúdo Web, por cada individuo, tem um impacto considerável no nosso quotidiano. Com o inegável sucesso de redes sociais e microblogs, como por exemplo Facebook, Instagram e Twitter, o fenómeno de influência exercida, por utilizadores de tais plataformas, em outros utilizadores e como se propaga na rede tem atraído, por alguns anos, informáticos, técnicos de informação e especialistas em marketing. O aumento da conectividade, o acesso multi-modal e a proliferação dos meios de comunicação social reduziram a distância entre quase todas as pessoas do mundo, mais e mais conteúdo é gerado. Extrair e analisar uma grande quantidade de dados não é uma tarefa trivial, são essenciais técnicas de Big Data. Através da análise desta interação, troca de informações e emoções, é perfeitamente imaginável a sua utilidade na compreensão de complexos comportamentos humanos e, portanto, ajudar na tomada de decisão de diversas organizações. A maximização da influência e o marketing viral estão entre as possibilidades. Este trabalho destina-se a estudar qual é o impacto e o papel que a influência social de um evento tem e como se propaga, particularmente no território envolvente. Esta influência é inferida pela análise dos dados de plataformas online, aplicando técnicas inteligentes, logo após a sua extração . O passo final é validar os resultados com dados de diferentes fontes. Ajudar empresas através do conhecimento valioso e atuável é o objetivo final. Este documento contempla uma seção introdutória, onde o assunto de estudo e o seu estado da arte são abordados. De seguida, é discutido o problema e a direção a seguir para o solucionar

The Web of False Information: Rumors, Fake News, Hoaxes, Clickbait, and Various Other Shenanigans

A new era of Information Warfare has arrived. Various actors, including state-sponsored ones, are weaponizing information on Online Social Networks to run false information campaigns with targeted manipulation of public opinion on specific topics. These false information campaigns can have dire consequences to the public: mutating their opinions and actions, especially with respect to critical world events like major elections. Evidently, the problem of false information on the Web is a crucial one, and needs increased public awareness, as well as immediate attention from law enforcement agencies, public institutions, and in particular, the research community. In this paper, we make a step in this direction by providing a typology of the Web's false information ecosystem, comprising various types of false information, actors, and their motives. We report a comprehensive overview of existing research on the false information ecosystem by identifying several lines of work: 1) how the public perceives false information; 2) understanding the propagation of false information; 3) detecting and containing false information on the Web; and 4) false information on the political stage. In this work, we pay particular attention to political false information as: 1) it can have dire consequences to the community (e.g., when election results are mutated) and 2) previous work show that this type of false information propagates faster and further when compared to other types of false information. Finally, for each of these lines of work, we report several future research directions that can help us better understand and mitigate the emerging problem of false information dissemination on the Web

The Dark Side(-Channel) of Mobile Devices: A Survey on Network Traffic Analysis

In recent years, mobile devices (e.g., smartphones and tablets) have met an increasing commercial success and have become a fundamental element of the everyday life for billions of people all around the world. Mobile devices are used not only for traditional communication activities (e.g., voice calls and messages) but also for more advanced tasks made possible by an enormous amount of multi-purpose applications (e.g., finance, gaming, and shopping). As a result, those devices generate a significant network traffic (a consistent part of the overall Internet traffic). For this reason, the research community has been investigating security and privacy issues that are related to the network traffic generated by mobile devices, which could be analyzed to obtain information useful for a variety of goals (ranging from device security and network optimization, to fine-grained user profiling). In this paper, we review the works that contributed to the state of the art of network traffic analysis targeting mobile devices. In particular, we present a systematic classification of the works in the literature according to three criteria: (i) the goal of the analysis; (ii) the point where the network traffic is captured; and (iii) the targeted mobile platforms. In this survey, we consider points of capturing such as Wi-Fi Access Points, software simulation, and inside real mobile devices or emulators. For the surveyed works, we review and compare analysis techniques, validation methods, and achieved results. We also discuss possible countermeasures, challenges and possible directions for future research on mobile traffic analysis and other emerging domains (e.g., Internet of Things). We believe our survey will be a reference work for researchers and practitioners in this research field.Comment: 55 page