#FoodPorn: Obesity Patterns in Culinary Interactions

We present a large-scale analysis of Instagram pictures taken at 164,753 restaurants by millions of users. Motivated by the obesity epidemic in the United States, our aim is three-fold: (i) to assess the relationship between fast food and chain restaurants and obesity, (ii) to better understand people's thoughts on and perceptions of their daily dining experiences, and (iii) to reveal the nature of social reinforcement and approval in the context of dietary health on social media. When we correlate the prominence of fast food restaurants in US counties with obesity, we find the Foursquare data to show a greater correlation at 0.424 than official survey data from the County Health Rankings would show. Our analysis further reveals a relationship between small businesses and local foods with better dietary health, with such restaurants getting more attention in areas of lower obesity. However, even in such areas, social approval favors the unhealthy foods high in sugar, with donut shops producing the most liked photos. Thus, the dietary landscape our study reveals is a complex ecosystem, with fast food playing a role alongside social interactions and personal perceptions, which often may be at odds

Exploration de la dynamique humaine basée sur des données massives de réseaux sociaux de géolocalisation : analyse et applications

Human dynamics is an essential aspect of human centric computing. As a transdisciplinary research field, it focuses on understanding the underlying patterns, relationships, and changes of human behavior. By exploring human dynamics, we can understand not only individual’s behavior, such as a presence at a specific place, but also collective behaviors, such as social movement. Understanding human dynamics can thus enable various applications, such as personalized location based services. However, before the availability of ubiquitous smart devices (e.g., smartphones), it is practically hard to collect large-scale human behavior data. With the ubiquity of GPS-equipped smart phones, location based social media has gained increasing popularity in recent years, making large-scale user activity data become attainable. Via location based social media, users can share their activities as real-time presences at Points of Interests (POIs), such as a restaurant or a bar, within their social circles. Such data brings an unprecedented opportunity to study human dynamics. In this dissertation, based on large-scale location centric social media data, we study human dynamics from both individual and collective perspectives. From individual perspective, we study user preference on POIs with different granularities and its applications in personalized location based services, as well as the spatial-temporal regularity of user activities. From collective perspective, we explore the global scale collective activity patterns with both country and city granularities, and also identify their correlations with diverse human culturesLa dynamique humaine est un sujet essentiel de l'informatique centrée sur l’homme. Elle se concentre sur la compréhension des régularités sous-jacentes, des relations, et des changements dans les comportements humains. En analysant la dynamique humaine, nous pouvons comprendre non seulement des comportements individuels, tels que la présence d’une personne à un endroit précis, mais aussi des comportements collectifs, comme les mouvements sociaux. L’exploration de la dynamique humaine permet ainsi diverses applications, entre autres celles des services géo-dépendants personnalisés dans des scénarios de ville intelligente. Avec l'omniprésence des smartphones équipés de GPS, les réseaux sociaux de géolocalisation ont acquis une popularité croissante au cours des dernières années, ce qui rend les données de comportements des utilisateurs disponibles à grande échelle. Sur les dits réseaux sociaux de géolocalisation, les utilisateurs peuvent partager leurs activités en temps réel avec par l'enregistrement de leur présence à des points d'intérêt (POIs), tels qu’un restaurant. Ces données d'activité contiennent des informations massives sur la dynamique humaine. Dans cette thèse, nous explorons la dynamique humaine basée sur les données massives des réseaux sociaux de géolocalisation. Concrètement, du point de vue individuel, nous étudions la préférence de l'utilisateur quant aux POIs avec des granularités différentes et ses applications, ainsi que la régularité spatio-temporelle des activités des utilisateurs. Du point de vue collectif, nous explorons la forme d'activité collective avec les granularités de pays et ville, ainsi qu’en corrélation avec les cultures globale

Sentiment Analysis of Nigerian Students’ Tweets on Education: A Data Mining Approach

The paper is aimed at investigating data mining technologies by acquiring tweets from Nigerian University students on Twitter on how they feel about the current state of the Nigerian university system. The study for this paper was conducted in a way that the tweet data collected using the Twitter Application was pre-processed before being translated from text to vector representation using a feature extraction technique such Bag-of-Words. In the paper, the proposed sentiment analysis architecture was designed using UML and the Naïve Bayes classifier (NBC) approach, which is a simple but effective classifier to determine the polarity of the education dataset, was applied to compute the probabilities of the classes. Furthermore, Naïve Bayes classifier polarized the tweets' wording as negative or positive for polarity. Based on our investigation, the experiment revealed after data cleaning that 4016 of the total data obtained were utilized. Also, Positive attitudes accounted for 40.56%, while negative sentiments accounted for 59.44% of the total data having divided the dataset into 70:30 training and testing ratio, with the Naïve Bayes classifier being taught on the training set and its performance being evaluated on the test set. Because the models were trained on unbalanced data, we employed more relevant evaluation metrics such as precision, recall, F1-score, and balanced accuracy for model evaluation. The classifier's prediction accuracy, misclassification error rate, recall, precision, and f1-score were 63 %, 37%, 63%, 62%, and 62% respectively. All of the analyses were completed using the Python programming language and the Natural Language Tool Kit packages. Finally, the outcome of this prediction is the highest likelihood class. These forecasts can be used by Nigerian Government to improve the educational system and assist students to receive a better education

Can Tourist Attractions Boost Other Activities Around? A Data Analysis through Social Networks

[EN] Promoting a tourist destination requires uncovering travel patterns and destination choices, identifying the profile of visitors and analyzing attitudes and preferences of visitors for the city. To this end, tourism-related data are an invaluable asset to understand tourism behaviour, obtain statistical records and support decision-making for business around tourism. In this work, we study the behaviour of tourists visiting top attractions of a city in relation to the tourist influx to restaurants around the attractions. We propose to undertake this analysis by retrieving information posted by visitors in a social network and using an open access map service to locate the tweets in a influence area of the city. Additionally, we present a pattern recognition based technique to differentiate visitors and locals from the collected data from the social network. We apply our study to the city of Valencia in Spain and Berlin in Germany. The results show that, while in Valencia the most frequented restaurants are located near top attractions of the city, in Berlin, it is usually the case that the most visited restaurants are far away from the relevant attractions of the city. The conclusions from this study can be very insightful for destination marketers.This work is supported by the Spanish MINECO project TIN2017-88476-C2-1-R.Bustamante, A.; Sebastiá Tarín, L.; Onaindia De La Rivaherrera, E. (2019). Can Tourist Attractions Boost Other Activities Around? A Data Analysis through Social Networks. Sensors. 19(11):1-25. https://doi.org/10.3390/s19112612S1251911Travel and Tourism Competitiveness Report 2017http://reports.weforum.org/travel-and-tourism-competitiveness-report-2017/OECD Datahttps://data.oecd.org/Travel &Tourism: Economic Impact 2019 Worldhttps://www.wttc.org/-/media/files/reports/economic-impact-research/regions-2019/world2019.pdfCohen, S. A., Prayag, G., & Moital, M. (2013). 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Tourism Management, 21(1), 97-116. doi:10.1016/s0261-5177(99)00095-3Indicators for Measuring Competitiveness in Tourism: A Guidance Documenthttp://dx.doi.org/10.1787/5k47t9q2t923-enLonghi, C., Titz, J.-B., & Viallis, L. (2014). Open Data: Challenges and Opportunities for the Tourism Industry. Tourism Management, Marketing, and Development, 57-76. doi:10.1057/9781137354358_4Open Data in Tourismhttps://www.europeandataportal.eu/en/highlights/open-data-tourismCox, C., Burgess, S., Sellitto, C., & Buultjens, J. (2009). The Role of User-Generated Content in Tourists’ Travel Planning Behavior. Journal of Hospitality Marketing & Management, 18(8), 743-764. doi:10.1080/19368620903235753Lu, W., & Stepchenkova, S. (2014). User-Generated Content as a Research Mode in Tourism and Hospitality Applications: Topics, Methods, and Software. Journal of Hospitality Marketing & Management, 24(2), 119-154. doi:10.1080/19368623.2014.907758Pantano, E., Priporas, C.-V., & Stylos, N. (2017). ‘You will like it!’ using open data to predict tourists’ response to a tourist attraction. Tourism Management, 60, 430-438. doi:10.1016/j.tourman.2016.12.020Hawelka, B., Sitko, I., Beinat, E., Sobolevsky, S., Kazakopoulos, P., & Ratti, C. (2014). Geo-located Twitter as proxy for global mobility patterns. Cartography and Geographic Information Science, 41(3), 260-271. doi:10.1080/15230406.2014.890072Girardin, F., Calabrese, F., Fiore, F. D., Ratti, C., & Blat, J. (2008). Digital Footprinting: Uncovering Tourists with User-Generated Content. IEEE Pervasive Computing, 7(4), 36-43. doi:10.1109/mprv.2008.71Alivand, M., & Hochmair, H. H. (2016). Spatiotemporal analysis of photo contribution patterns to Panoramio and Flickr. Cartography and Geographic Information Science, 44(2), 170-184. doi:10.1080/15230406.2016.1211489Bassolas, A., Lenormand, M., Tugores, A., Gonçalves, B., & Ramasco, J. J. (2016). Touristic site attractiveness seen through Twitter. EPJ Data Science, 5(1). doi:10.1140/epjds/s13688-016-0073-5Mariani, M., Baggio, R., Fuchs, M., & Höepken, W. (2018). Business intelligence and big data in hospitality and tourism: a systematic literature review. International Journal of Contemporary Hospitality Management, 30(12), 3514-3554. doi:10.1108/ijchm-07-2017-0461Francalanci, C., & Hussain, A. (2015). Discovering social influencers with network visualization: evidence from the tourism domain. Information Technology & Tourism, 16(1), 103-125. doi:10.1007/s40558-015-0030-3Williams, N. L., Inversini, A., Ferdinand, N., & Buhalis, D. (2017). Destination eWOM: A macro and meso network approach? Annals of Tourism Research, 64, 87-101. doi:10.1016/j.annals.2017.02.007Salas-Olmedo, M. H., Moya-Gómez, B., García-Palomares, J. C., & Gutiérrez, J. (2018). Tourists’ digital footprint in cities: Comparing Big Data sources. Tourism Management, 66, 13-25. doi:10.1016/j.tourman.2017.11.001Padilla, J. J., Kavak, H., Lynch, C. J., Gore, R. J., & Diallo, S. Y. (2018). Temporal and spatiotemporal investigation of tourist attraction visit sentiment on Twitter. PLOS ONE, 13(6), e0198857. doi:10.1371/journal.pone.0198857Maeda, T., Yoshida, M., Toriumi, F., & Ohashi, H. (2018). Extraction of Tourist Destinations and Comparative Analysis of Preferences Between Foreign Tourists and Domestic Tourists on the Basis of Geotagged Social Media Data. ISPRS International Journal of Geo-Information, 7(3), 99. doi:10.3390/ijgi7030099Wöber, K. W. (2003). Information supply in tourism management by marketing decision support systems. Tourism Management, 24(3), 241-255. doi:10.1016/s0261-5177(02)00071-7Sabou, M., Onder, I., Brasoveanu, A. M. P., & Scharl, A. (2016). Towards cross-domain data analytics in tourism: a linked data based approach. Information Technology & Tourism, 16(1), 71-101. doi:10.1007/s40558-015-0049-5Adamiak, C., Szyda, B., Dubownik, A., & García-Álvarez, D. (2019). Airbnb Offer in Spain—Spatial Analysis of the Pattern and Determinants of Its Distribution. ISPRS International Journal of Geo-Information, 8(3), 155. doi:10.3390/ijgi8030155Padron Municipal de Habitantes [Statistical Report: Residents in Valencia in 2018]https://bit.ly/2JnNNE

Social Media data: Challenges, opportunities and limitations in urban studies

Analysing the city through data retrieved from Location Based Social Networks (LBSNs) has received considerable attention as a promising method for applied research. However, the use of these data is not without its challenges and has given rise to a stream of polemical arguments over the validity of this source of information. This paper addresses the challenges and opportunities as well as some of the limitations and biases associated with the collection and use of LBSN data from Foursquare, Twitter, Google Places, Instagram and Airbnb in the context of urban phenomena research. The most recent research that uses LBSN data to understand city dynamics is presented. A method is proposed for LBSN data retrieval, selection, classification and analysis. In addition, key thematic research lines are identified given the data variables offered by these LBSNs. A comprehensive and descriptive framework for the study of urban phenomena through LBSN data is the main contribution of this study.This work was supported by the Council of Education, Research, Culture and Sports – Generalitat Valenciana (Spain). Project: Valencian Community cities analysed through Location-Based Social Networks and Web Services Data. Ref. no. AICO/2017/018

Brand sabotage: Managing social media and reputational crises in utility companies

In 2011, the market leader in the Portuguese energy sector decided to delete its presence on Facebook, its most active social media platform, after a poorly perceived social media management decision went viral and unleashed a series of accusatory comments that harmed the company’s brand and reputation. Today, in 2018, this company is still opting not to be fully present on its social media platforms, revealing that the effects of the 2011 crisis were both long-lasting and harmful for the company’s image with its main stakeholders. In this thesis, we develop a set of best practices in social media management that can help prevent social media crises in the Portuguese energy sector and, simultaneously, provide energy companies with the tools to improve their brand awareness, image, and reputation through social media platforms in the current digital and globalized economy. We start by characterizing the main challenges faced by utility companies on their daily social media activities. Related to this, we identify the potential channels that can lead these companies into social media crises, and we study the best actions undertaken by the market leaders in the Spanish and UK energy markets against these reputation threats. To do so, we undertake a quantitative netnography analysis on these markets, using state-of-the-art data scrapping and text analytics techniques. Finally, we use the main results from the netnography analysis to clearly define the most important social media strategies followed by the Spanish and UK energy market leaders. We highlight the managerial implications of our analysis by developing a unifying social media strategy to help Portuguese energy companies prevent new social media crises and to allow them to effectively manage their brand awareness and reputation by using social media platforms. We conclude with the implementation of this strategy using a best practices framework that Portuguese energy companies could follow in the near future.No ano de 2011, a empresa líder no mercado de energia em Portugal, colocou um fim à sua presença ativa nas redes sociais, após a tomada de uma decisão estratégica que levou à massificação de críticas de clientes e seguidores. Atualmente, a empresa continua cautelosa em estar nas redes sociais, mantendo apenas uma presença limitada no YouTube e, mais recentemente, através de uma página de Instagram específica para divulgação de eventos patrocinados ou desenvolvidos pela mesma. Esta estratégia de comunicação online da EDP, revela que os eventos vividos em 2011 foram duradouros e prejudiciais para a reputação e imagem da mesma junto dos seus stakeholders. Nesta dissertação, é desenvolvido um conjunto de práticas para uma melhor gestão de redes sociais, com foco especial no Facebook. Esta análise tem como objetivo ajudar as empresas de Energia Portuguesas a prevenir crises virais relacionadas com a sua política de comunicação nestas plataformas e, por conseguinte, melhorar a brand awareness e reputação destas empresas no contexto de uma economia digital e globalizada. Para tal, caracterizamos os principais desafios de diferentes estratégias de comunicação, envolvendo redes sociais por parte de empresas internacionais no setor da Energia. Consequentemente, são então identificados os potenciais canais e ações destas empresas que poderão deteriorar a relação da empresa com os seus stakeholders e levar, eventualmente, a uma situação de crise. Para fazer face a tal possibilidade, são estudados exemplos de empresas líderes no setor de energia no Reino Unido e em Espanha, através de uma netnografia quantitativa, utilizando técnicas de data scrapping e text analytics. Através desta análise, são realçadas as principais melhores práticas que poderão ajudar empresas Portuguesas a prevenir eventuais crises de comunicação online nas suas páginas e plataformas sociais. O objetivo último desta dissertação é permitir a estas empresas gerir eficazmente a sua brand awareness e reputação e, simultaneamente, fomentar de forma eficiente e transparente a sua relação com os seus stakeholders

Social media cross-source and cross-domain sentiment classification

Due to the expansion of Internet and Web 2.0 phenomenon, there is a growing interest in the sentiment analysis of freely opinionated text. In this paper, we propose a novel cross-source cross-domain sentiment classification, in which cross-domain labeled Web sources (Amazon and Tripadvisor) are used to train supervised learning models (including two deep learning algorithms) that are tested on typically non labeled social media reviews (Facebook and Twitter). We explored a three step methodology, in which dis- tinct balanced training, text preprocessing and machine learning methods were tested, using two languages: English and Italian. The best results were achieved when using undersampling training and a Convolutional Neural Network. Interesting cross-source classification performances were achieved, in particular when using Amazon and Tripadvisor reviews to train a model that is tested on Facebook data for both English and Italian.Research carried out with the support of resources of Big&Open Data Innovation Laboratory (BODaI-Lab), the University of Brescia, granted by Fondazione Cariplo and Regione Lombardia. The work of P. Cortez was supported by FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope UID/CEC/00319/2019. We would also like to thank the three anonymous reviewers for their helpful suggestions

Hermes: Distributed social network monitoring system

[ANGLÈS] Nowadays, social network services play a very important role in the way people interact with each other and with the world. This generates big amounts of data that can be used to study social relationships and extract useful information about preferences and trends. When analysing this information, two main problems emerge: The need to aggregate dif- ferent data coming from multiple sources, and hardware limitations due to the incapability traditional systems have to deal with large amounts of data. In order to solve the problems mentioned before, this project aims to implement a distributed, scalable social media analysis tool, ready to connect and gather data from multiple sources and show the aggregated results in real-time.[CASTELLÀ] Hoy en día, las redes sociales juegan un papel muy importante en la manera como las personas interactúa entre ellos y con el mundo. Esto genera grandes volúmenes de inforación que pueden ser utilizados para estudiar las relaciones sociales y extraer información útil acerca de gustos y tendencias. Cuando se analiza esta información, surgen dos problemas principales: La necesidad de agregar diferentes datos provenientes de múltiples fuentes, y las limitaciones hardware por la incapacidad de los sistemas tradicionales de manejar grandes cantidades de datos. Para poder solventar estos problemas, este proyecto propone implementar una herramienta de análisis de redes sociales distribuida y escalable, preparada para conectarse y recolectar datos de múltiples fuentes y mostrar los resultados agregados en tiempo real.[CATALÀ] Avui en dia, les xarxes socials juguen un paper molt important en la manera com les persones interactua entre ells i amb el mon. Això genera grans quantitats de dades que poden ser utilitzats per estudiar les relacions socials i extreure informació útil sobre gustos i tendències. 2 Quan s’analitza aquesta informació, sorgeixen dos problemes principals: La necessitat de agregar diferents dades provinents de múltiples fonts, i les limitacions hardware per la incapaci- tat dels sistemes tradicionals de gestionar grans quantitats de dades. Per poder solucionar aque- sts problemes, aquest projecte proposa implementar una eina d’anàlisi de xarxes socials dis- tribuïda i escalable, preparada per connectar-se i recol·lectar dades de múltiples fonts i mostrar els resultats agregats en temps real

On the development of an information system for monitoring user opinion and its role for the public

Social media services and analytics platforms are rapidly growing. A large number of various events happen mostly every day, and the role of social media monitoring tools is also increasing. Social networks are widely used for managing and promoting brands and different services. Thus, most popular social analytics platforms aim for business purposes while monitoring various social, economic, and political problems remains underrepresented and not covered by thorough research. Moreover, most of them focus on resource-rich languages such as the English language, whereas texts and comments in other low-resource languages, such as the Russian and Kazakh languages in social media, are not represented well enough. So, this work is devoted to developing and applying the information system called the OMSystem for analyzing users' opinions on news portals, blogs, and social networks in Kazakhstan. The system uses sentiment dictionaries of the Russian and Kazakh languages and machine learning algorithms to determine the sentiment of social media texts. The whole structure and functionalities of the system are also presented. The experimental part is devoted to building machine learning models for sentiment analysis on the Russian and Kazakh datasets. Then the performance of the models is evaluated with accuracy, precision, recall, and F1-score metrics. The models with the highest scores are selected for implementation in the OMSystem. Then the OMSystem's social analytics module is used to thoroughly analyze the healthcare, political and social aspects of the most relevant topics connected with the vaccination against the coronavirus disease. The analysis allowed us to discover the public social mood in the cities of Almaty and Nur-Sultan and other large regional cities of Kazakhstan. The system's study included two extensive periods: 10-01-2021 to 30-05-2021 and 01-07-2021 to 12-08-2021. In the obtained results, people's moods and attitudes to the Government's policies and actions were studied by such social network indicators as the level of topic discussion activity in society, the level of interest in the topic in society, and the mood level of society. These indicators calculated by the OMSystem allowed careful identification of alarming factors of the public (negative attitude to the government regulations, vaccination policies, trust in vaccination, etc.) and assessment of the social mood

Machine translation of user-generated content

The world of social media has undergone huge evolution during the last few years. With the spread of social media and online forums, individual users actively participate in the generation of online content in different languages from all over the world. Sharing of online content has become much easier than before with the advent of popular websites such as Twitter, Facebook etc. Such content is referred to as ‘User-Generated Content’ (UGC). Some examples of UGC are user reviews, customer feedback, tweets etc. In general, UGC is informal and noisy in terms of linguistic norms. Such noise does not create significant problems for human to understand the content, but it can pose challenges for several natural language processing applications such as parsing, sentiment analysis, machine translation (MT), etc. An additional challenge for MT is sparseness of bilingual (translated) parallel UGC corpora. In this research, we explore the general issues in MT of UGC and set some research goals from our findings. One of our main goals is to exploit comparable corpora in order to extract parallel or semantically similar sentences. To accomplish this task, we design a document alignment system to extract semantically similar bilingual document pairs using the bilingual comparable corpora. We then apply strategies to extract parallel or semantically similar sentences from comparable corpora by transforming the document alignment system into a sentence alignment system. We seek to improve the quality of parallel data extraction for UGC translation and assemble the extracted data with the existing human translated resources. Another objective of this research is to demonstrate the usefulness of MT-based sentiment analysis. However, when using openly available systems such as Google Translate, the translation process may alter the sentiment in the target language. To cope with this phenomenon, we instead build fine-grained sentiment translation models that focus on sentiment preservation in the target language during translation