Analyzing the emotional impact of COVID-19 with Twitter data: Lessons from a B-VAR analysis on Italy

: The novel coronavirus 2019 revolutionized the way of living and the communication of people making social media a popular tool to express concerns and perceptions. Starting from this context we built an original database based on the Twitter users' emotions shown in the early weeks of the pandemic in Italy. Specifically, using a single index we measured the feelings of four groups of stakeholders (journalists, people, doctors, and politicians), in three groups of Italian regions (0,1,2), grouped according to the impact of the COVID-19 crises as defined by the Conte Government Ministerial Decree (8th March 2020). We then applied B-VAR techniques to analyze the sentiment relationships between the groups of stakeholders in every Region Groups. Results show a high influence of doctors at the beginning of the epidemic in the Group that includes most of Italian regions (Group 0), and in Lombardy that has been the region of Italy hit the most by the pandemic (Group 2). Our outcomes suggest that, given the role played by stakeholders and the COVID-19 magnitude, health policy interventions based on communication strategies may be used as best practices to develop regional mitigation plans for the containment and contrast of epidemiological emergencies

Assessing the market performance of social media, monitoring dashboards : identification of key attributes

Which technical features drive market demand for social media monitoring dashboards (SMMDs)? Social networks are a “door of opportunity” to establish a product or brand through social media, and to engage new consumers. Social media makes it easier to monitor, collect, analyze, and manage sentiment, attitudes, complaints, software inquiries, and discussions. A measurement strategy is a fundamental factor to obtain these types of information. Although there are alternative ways to gather this data, different SMMDs enable data analysis, allowing management to glean business insights that enables enterprises to capitalize on all this information to make informed decisions. This dissertation includes key features that can be used to analyze SMMDs. A literature review helped to identify research questions for this study. Consequently, questions were formulated in the interview script addressed to informants in Colombia and Portugal. This interview filtered key features identified as most important when facing the need to choose a dashboard. Once the information was gathered, a survey was designed. The survey focused around three key features: visualization/user interface, real-time processing, and cost. These attributes emerged from an analysis of the respondents’ survey. Responses were then categorized, and themes began to be gleaned from the respondents’ answers. Finally, these categories were evaluated by community managers (CMs), which total number of collected samples was 169 surveys. The intentionality behind this methodology was to discover a criteria by which to select dashboards, concluding real-time processing as a key feature chosen by 69,2% of responders. Respondents chose it as a fundamental attribute. Another relevant issue during analysis, considered by CMs, based on years of experience with the tool, was Hootsuite since it is a well-known dashboard, with a mean of 3,39, and a σ=1,22. This choice represented approximately 33%, which made it the most preferred tool by CMs at middle to large companies.Quais os aspetos técnicos que encaminham a procura do mercado para os painéis de gestão das redes sociais? As redes sociais estão a tornar-se um meio de oportunidade para estabelecer um produto ou marca e cativar novos clientes. Com as redes sociais é mais fácil monitorizar, recolher dados, analisar e gerir os sentimentos, atitudes, queixas, bem como dúvidas acerca do software. Uma estratégia de medição é um fator fundamental para obter todos estes tipos de informação. Apesar de haver métodos alternativos, os diferentes painéis de gestão das redes sociais permitem a análise de dados e recolha de informação negocial por parte da equipa de gestão de modo a que a empresa seja capaz de interpretar toda esta informação e tomar decisões informadas. Esta dissertação inclui aspetos chave que podem ser usados para analisar os painéis de gestão das redes sociais. Foi realizada uma revisão bibliográfica detalhada e identificadas várias perguntas de pesquisa. Consequentemente, foram formuladas perguntas no guião da entrevista visando informadores da Colombia e de Portugal. Esta entrevista filtrou os aspetos chave identificados como os mais importantes ao enfrentar a escolha de um painel de gestão. Assim que a informação foi recolhida, foi desenvolvido um inquérito. O inquérito focou-se em três aspetos chave: visualização/interface do usuário, processamento em tempo real e custo. Este atributos surgiram de uma análise dos inquéritos dos respondentes. As respostas foram então categorizadas e começaram a ser identificados temas a partir das mesmas. Por fim, estas categorias foram avaliadas pelos gestores da comunidade, onde o número de amostras recolhidas foi de 169. A intenção por detrás desta metodologia era descobrir o critério segundo o qual proceder à seleção dos painéis de gestão, concluindo-se que um aspeto chave é o processamento em tempo real com 69,2% dos respondedores, que a escolheu como um atributo fundamental. Outro assunto relevante considerado na análise foi o Hootsuite, uma vez que é um painel de gestão bastante reconhecido, com uma média de 3,39, e uma σ = 1,22, tendo vários anos de experiência em ele os respondentes, e representa cerca de 33%. Isto significa que é o painel mais preferido pelos gestores da comunidade em empresas de média a grande dimensão

A SYSTEMATIC REVIEW OF COMPUTATIONAL METHODS IN AND RESEARCH TAXONOMY OF HOMOPHILY IN INFORMATION SYSTEMS

Homophily is both a principle for social group formation with like-minded people as well as a mechanism for social interactions. Recent years have seen a growing body of management research on homophily particularly on large-scale social media and digital platforms. However, the predominant traditional qualitative and quantitative methods employed face validity issues and/or are not well-suited for big social data. There are scant guidelines for applying computational methods to specific research domains concerning descriptive patterns, explanatory mechanisms, or predictive indicators of homophily. To fill this research gap, this paper offers a structured review of the emerging literature on computational social science approaches to homophily with a particular emphasis on their relevance, appropriateness, and importance to information systems research. We derive a research taxonomy for homophily and offer methodological reflections and recommendations to help inform future research

Text-based Sentiment Analysis and Music Emotion Recognition

Nowadays, with the expansion of social media, large amounts of user-generated texts like tweets, blog posts or product reviews are shared online. Sentiment polarity analysis of such texts has become highly attractive and is utilized in recommender systems, market predictions, business intelligence and more. We also witness deep learning techniques becoming top performers on those types of tasks. There are however several problems that need to be solved for efficient use of deep neural networks on text mining and text polarity analysis. First of all, deep neural networks are data hungry. They need to be fed with datasets that are big in size, cleaned and preprocessed as well as properly labeled. Second, the modern natural language processing concept of word embeddings as a dense and distributed text feature representation solves sparsity and dimensionality problems of the traditional bag-of-words model. Still, there are various uncertainties regarding the use of word vectors: should they be generated from the same dataset that is used to train the model or it is better to source them from big and popular collections that work as generic text feature representations? Third, it is not easy for practitioners to find a simple and highly effective deep learning setup for various document lengths and types. Recurrent neural networks are weak with longer texts and optimal convolution-pooling combinations are not easily conceived. It is thus convenient to have generic neural network architectures that are effective and can adapt to various texts, encapsulating much of design complexity. This thesis addresses the above problems to provide methodological and practical insights for utilizing neural networks on sentiment analysis of texts and achieving state of the art results. Regarding the first problem, the effectiveness of various crowdsourcing alternatives is explored and two medium-sized and emotion-labeled song datasets are created utilizing social tags. One of the research interests of Telecom Italia was the exploration of relations between music emotional stimulation and driving style. Consequently, a context-aware music recommender system that aims to enhance driving comfort and safety was also designed. To address the second problem, a series of experiments with large text collections of various contents and domains were conducted. Word embeddings of different parameters were exercised and results revealed that their quality is influenced (mostly but not only) by the size of texts they were created from. When working with small text datasets, it is thus important to source word features from popular and generic word embedding collections. Regarding the third problem, a series of experiments involving convolutional and max-pooling neural layers were conducted. Various patterns relating text properties and network parameters with optimal classification accuracy were observed. Combining convolutions of words, bigrams, and trigrams with regional max-pooling layers in a couple of stacks produced the best results. The derived architecture achieves competitive performance on sentiment polarity analysis of movie, business and product reviews. Given that labeled data are becoming the bottleneck of the current deep learning systems, a future research direction could be the exploration of various data programming possibilities for constructing even bigger labeled datasets. Investigation of feature-level or decision-level ensemble techniques in the context of deep neural networks could also be fruitful. Different feature types do usually represent complementary characteristics of data. Combining word embedding and traditional text features or utilizing recurrent networks on document splits and then aggregating the predictions could further increase prediction accuracy of such models

Measuring Social Well Being in The Big Data Era: Asking or Listening?

The literature on well being measurement seems to suggest that "asking" for a self-evaluation is the only way to estimate a complete and reliable measure of well being. At the same time "not asking" is the only way to avoid biased evaluations due to self-reporting. Here we propose a method for estimating the welfare perception of a community simply "listening" to the conversations on Social Network Sites. The Social Well Being Index (SWBI) and its components are proposed through to an innovative technique of supervised sentiment analysis called iSA which scales to any language and big data. As main methodological advantages, this approach can estimate several aspects of social well being directly from self-declared perceptions, instead of approximating it through objective (but partial) quantitative variables like GDP; moreover self-perceptions of welfare are spontaneous and not obtained as answers to explicit questions that are proved to bias the result. As an application we evaluate the SWBI in Italy through the period 2012-2015 through the analysis of more than 143 millions of tweets.Comment: 40 pages, 2 figures. arXiv admin note: text overlap with arXiv:1512.0156

Application of Natural Language Processing to Determine User Satisfaction in Public Services

Research on customer satisfaction has increased substantially in recent years. However, the relative importance and relationships between different determinants of satisfaction remains uncertain. Moreover, quantitative studies to date tend to test for significance of pre-determined factors thought to have an influence with no scalable means to identify other causes of user satisfaction. The gaps in knowledge make it difficult to use available knowledge on user preference for public service improvement. Meanwhile, digital technology development has enabled new methods to collect user feedback, for example through online forums where users can comment freely on their experience. New tools are needed to analyze large volumes of such feedback. Use of topic models is proposed as a feasible solution to aggregate open-ended user opinions that can be easily deployed in the public sector. Generated insights can contribute to a more inclusive decision-making process in public service provision. This novel methodological approach is applied to a case of service reviews of publicly-funded primary care practices in England. Findings from the analysis of 145,000 reviews covering almost 7,700 primary care centers indicate that the quality of interactions with staff and bureaucratic exigencies are the key issues driving user satisfaction across England