Graph-based Rumour Detection for Social Media

Social platforms became a major source of rumours. While rumours can have severe real-world implications, their detection is notoriously hard: Content on social platforms is short and lacks semantics; it spreads quickly through a dynamically evolving network; and without considering the context of content, it may be impossible to arrive at a truthful interpretation. Traditional approaches to rumour detection, however, exploit solely a single content modality, e.g., social media posts, which limits their detection accuracy. In this paper, we cope with the aforementioned challenges by means of a multi-modal approach to rumour detection that identifies anomalies in both, the entities (e.g., users, posts, and hashtags) of a social platform and their relations. Based on local anomalies, we show how to detect rumours at the network level, following a graph-based scan approach

A perspectiva do utilizador do Facebook relativamente à escolha de alojamento turístico

Atualmente assistimos a um aumento exponencial do uso dos social media como meio para planear as viagens lazer, bem como procurar por informações relativas a hotéis, restaurantes e outras empresas na indústria de turismo e viagens. Os social media criaram um novo canal de distribuição, tendo este influenciado e alterado a forma como os viajantes determinam o local onde vão ficar alojados. É importante que as empresas hoteleiras compreendam o comportamento dos consumidores face aos social media e, assim, determinar qual a forma de comunicação e que informações deverão disponibilizar nos seus sites. A título de exemplo, os hotéis conseguem interagir com os clientes através das redes sociais, como o Facebook, Instagram ou Youtube, partilhar diversos tipos de conteúdos informativos e prestar assistência a questões. O presente estudo tem como objetivo compreender o uso das redes sociais, apresentando-se um maior foco na rede social Facebook, na promoção de um estabelecimento hoteleiro e, com isto, determinar se a promoção dos serviços hoteleiros através deste meio, apresenta influência na tomada de decisão de escolha de alojamento turístico. Por outro lado, pretendese analisar o impacto das avaliações/recomendações realizadas pelos consumidores presentes no Facebook. Adotou-se uma metodologia quantitativa, através de um questionário online. Para analisar as hipóteses de estudo recorreu-se a diversos testes estatísticos. Os principais resultados demonstraram que são diversos os meios online e offline em que os consumidores se baseiam para fazer a sua decisão de escolha de alojamento, sendo um destes o Facebook, que apresenta uma grande representatividade. O word-of-mouth, contabilizado através das opiniões dos antigos clientes presentes em sites de avaliações e em redes sociais revela-se uma componente determinante no processo de recolha de informação sobre determinado alojamento e consequentemente influenciador na escolha de alojamento. Por último, verificou-se que o Facebook apresenta ter um papel decisivo no processo de decisão de escolha de alojamento turístico.Nowadays we face an exponential increase of the use of the social media as a mean to plan a trip, as well as, search for information about tourist accommodation, restaurants and other establishments in the traveling business. The social media have created a new distribution channel that has changed the way travellers choose the place they will stay. It is important that the hoteliers understand the consumer behavior towards the social media, in order to determine the way to communicate with the audience and which type of contents should be shared in theirs websites. As an example, hotels can communicate and interact with the clients through the social networks, such as, Facebook, Instagram or Youtube, and share different types of contents and manage the establishment image. The current study aims to understand the use of the social networks, focusing on the social network Facebook, in the promotion of an establishment and try to demonstrate if the promotion of the hotel services through this approach, shows influence in the process of choosing a place to stay. On the other side, the study intents to analyze the impact of the reviews made from the consumers that have an account in the website Facebook. We have adapted a quantitative methodology, though the use of an online query. To analyze the study hypothesis we have proceeded to several statistic tests. The main results have showed that there are several online and offline means that the consumers use to make their establishment decision, being one of them the Facebook. The word-of-mouth, that can be found through the opinions of the former clients, present in website reviews and social networks, that have showed to be a very important component in the process of gathering information about a specific place, and consequently influent the establishment decision. Lastly, we have showed that the Facebook plays a decisive roll in the choosing process decision of tourist accommodation

Real-time probabilistic reasoning system using Lambda architecture

Thesis (MTech (Information Technology))--Cape Peninsula University of Technology, 2019The proliferation of data from sources like social media, and sensor devices has become overwhelming for traditional data storage and analysis technologies to handle. This has prompted a radical improvement in data management techniques, tools and technologies to meet the increasing demand for effective collection, storage and curation of large data set. Most of the technologies are open-source. Big data is usually described as very large dataset. However, a major feature of big data is its velocity. Data flow in as continuous stream and require to be actioned in real-time to enable meaningful, relevant value. Although there is an explosion of technologies to handle big data, they are usually targeted at processing large dataset (historic) and real-time big data independently. Thus, the need for a unified framework to handle high volume dataset and real-time big data. This resulted in the development of models such as the Lambda architecture. Effective decision-making requires processing of historic data as well as real-time data. Some decision-making involves complex processes, depending on the likelihood of events. To handle uncertainty, probabilistic systems were designed. Probabilistic systems use probabilistic models developed with probability theories such as hidden Markov models with inference algorithms to process data and produce probabilistic scores. However, development of these models requires extensive knowledge of statistics and machine learning, making it an uphill task to model real-life circumstances. A new research area called probabilistic programming has been introduced to alleviate this bottleneck. This research proposes the combination of modern open-source big data technologies with probabilistic programming and Lambda architecture on easy-to-get hardware to develop a highly fault-tolerant, and scalable processing tool to process both historic and real-time big data in real-time; a common solution. This system will empower decision makers with the capacity to make better informed resolutions especially in the face of uncertainty. The outcome of this research will be a technology product, built and assessed using experimental evaluation methods. This research will utilize the Design Science Research (DSR) methodology as it describes guidelines for the effective and rigorous construction and evaluation of an artefact. Probabilistic programming in the big data domain is still at its infancy, however, the developed artefact demonstrated an important potential of probabilistic programming combined with Lambda architecture in the processing of big data

Realtime Analysis of Information Diffusion in Social Media Io Taxidou

The goal of this thesis is to investigate real-time analysis methods on social media with a focus on information diffusion. From a conceptual point of view, we are interested both in the structural, sociological and temporal aspects of information diffusion in social media with a twist on the real time factor of what is happening right now. From a technical side, the sheer size of current social media services (100’s of millions of users) and the large amount of data produced by these users renders conventional approaches for these costly analyses impossible. For that, we need to go beyond the state-of-the-art infrastructure for data-intensive computation. Our high level goal is to investigate how information diffuses in real time on the underlying social network and the role of different users in the propagation process. We plan to implement these analyses with full and partially missing datasets and compare the cost and quality of both approaches