3,334 research outputs found

    Discovering Periodic Patterns in Historical News

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    We address the problem of observing periodic changes in the behaviour of a large population, by analysing the daily contents of newspapers published in the United States and United Kingdom from 1836 to 1922. This is done by analysing the daily time series of the relative frequency of the 25K most frequent words for each country, resulting in the study of 50K time series for 31,755 days. Behaviours that are found to be strongly periodic include seasonal activities, such as hunting and harvesting. A strong connection with natural cycles is found, with a pronounced presence of fruits, vegetables, flowers and game. Periodicities dictated by religious or civil calendars are also detected and show a different wave-form than those provoked by weather. States that can be revealed include the presence of infectious disease, with clear annual peaks for fever, pneumonia and diarrhoea. Overall, 2% of the words are found to be strongly periodic, and the period most frequently found is 365 days. Comparisons between UK and US, and between modern and historical news, reveal how the fundamental cycles of life are shaped by the seasons, but also how this effect has been reduced in modern times

    Does \u2018bigger\u2019mean \u2018better\u2019? Pitfalls and shortcuts associated with big data for social research

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    \u2018Big data is here to stay.\u2019 This key statement has a double value: is an assumption as well as the reason why a theoretical reflection is needed. Furthermore, Big data is something that is gaining visibility and success in social sciences even, overcoming the division between humanities and computer sciences. In this contribution some considerations on the presence and the certain persistence of Big data as a socio-technical assemblage will be outlined. Therefore, the intriguing opportunities for social research linked to such interaction between practices and technological development will be developed. However, despite a promissory rhetoric, fostered by several scholars since the birth of Big data as a labelled concept, some risks are just around the corner. The claims for the methodological power of bigger and bigger datasets, as well as increasing speed in analysis and data collection, are creating a real hype in social research. Peculiar attention is needed in order to avoid some pitfalls. These risks will be analysed for what concerns the validity of the research results \u2018obtained through Big data. After a pars distruens, this contribution will conclude with a pars construens; assuming the previous critiques, a mixed methods research design approach will be described as a general proposal with the objective of stimulating a debate on the integration of Big data in complex research projecting

    Public Sentiments towards the COVID-19 Pandemic: Insights from the Academic Literature Review and Twitter Analytics

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    The recent COVID-19 pandemic has severely impacted nations across the globe. Not only has it created economic shocks, but also long-term impacts on the social and psychological behaviors of the public. This can be attributed to the severity of the pandemic and because of the preventive and control measures such as global lockdowns, social distancing, and selfisolation that the governments imposed. Previous studies have reported significant changes in human emotions and behaviors are used to measure public sentiments about certain phenomena (such as the recent pandemic). The present study aims to study the public's sentiments during the COVID-19 outbreak based on an analytics review of public tweets highlighting changes in emotions. A dataset of 58,320 tweets extracted from Twitter and 61 academic articles was explored to analyze behavioral and emotional changes during previous and current pandemic situations. We chose the RPA – COV (Research Process Approach – COVID-19) approach, which was combined with the LBTA (Literature-Based Thematic Analysis) and the COVTA (COVID-19 Twitter Analytics). The sentiments' analysis results were coupled with word-tree analysis and highlighted that the public showed more highly neutral, positive, and mixed emotions than negative ones. The analysis pointed that people may react differently on Twitter as compared to real-life circumstances. The present study makes a significant contribution towards understanding how the public express their sentiments in pandemic situations

    Transition of Blame: The Othering of AIDS from Homosexuals to Africans

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    In 1983, the first year The New York Times wrote more than one story on AIDS — acquired immune deficiency syndrome — the newspaper printed 77 articles that included the word “AIDS” and the word “homosexual.” This total reached its peak in 1987, when 314 articles that included the two words were written. In 1990, this total was down to 109, and at the turn of the century in 2000, only 29 articles that mentioned these two words were published. Conversely, in 1983, only nine Times articles included “AIDS” and “Africa.” In 1987, when articles about the connection between AIDS and homosexuality were at a climax, 175 articles in The New York Times mentioned AIDS and Africa. As the link between AIDS and homosexuals tapered off in 2000, the article total for “AIDS” and “Africa” was at an all-time high of 270. The objective of this thesis was to answer the question of why this shift in focus occurred. This objective was achieved by analyzing specific New York Times articles that were printed from 1980 until 2000 and by studying the overall history of the AIDS epidemic. As comparison, articles from The San Francisco Chronicle were also examined to see if a similar shift of focus occurred in this paper’s coverage of the AIDS epidemic. It was determined that the shift in coverage mirrored the disease’s epidemiological shift. During the late 1980s and early 1990s, AIDS’s prevalence transitioned from the United States to Africa, which was greatly influenced by the development of a drug that stalled HIV’s mutation into AIDS. This drug was widely used in the United States, but proved too expensive for common use in Africa. While the geographical shift of the disease explains — on the surface — the newspapers’ shift of coverage of AIDS, it was discovered that The New York Times’ initial coverage of AIDS’s occurrence in homosexuals set a template of “othering” that the paper followed in its later coverage of the disease’s presence in Africa. In comparison, The San Francisco Chronicle did not follow this template. Instead of bluntly stating the facts of the epidemic’s progression and relying heavily on the official statements of government organizations in its articles, which helped demonize the disease and its patients, The Chronicle took a more anecdotal method of reporting the AIDS epidemic. While the Chronicle’s people-based covered helped stall the development of stereotypes and stigmas surrounding AIDS more so than articles published by The New York Times, both papers failed to put the AIDS epidemic and assumptions that developed about its victims in their historical and cultural context. A historical and cultural examination of the homophobic and xenophobic tendencies of the American public and its media was completed in Chapter Four of this thesis. These examinations identified the Christian undertones and moral code that have saturated the nation and its governmental decisions since the United States’ inception. Such moral standards were at an all-time high when the AIDS epidemic emerged in the early 1980s at the start of the Culture Wars. These standards appeared in the United States government’s reaction to the epidemic and the news media’s coverage of it. In conclusion, The New York Times and The San Francisco Chronicle followed the geographical transition of the disease and reflected the country’s moral beliefs at the time AIDS first appeared. While the mystery surrounding AIDS, the puzzling nature of the disease and the overall Christian code of the United States excused most of the newspapers’ shortcomings in reporting, both papers — by being in existence — have assumed the role of the Fourth Estate. Their duty is to question and criticize both governmental and societal trends and beliefs. In the case of AIDS and the people whose lives it took, The New York Times and The San Francisco Chronicle failed in this gate-keeping role, and instead helped perpetuate the stereotypes surrounding the epidemic that still plague the nation today

    Public Sentiments towards the COVID-19 Pandemic: Insights from the Academic Literature Review and Twitter Analytics

    Get PDF
    The recent COVID-19 pandemic has severely impacted nations across the globe. Not only has it created economic shocks, but also long-term impacts on the social and psychological behaviors of the public. This can be attributed to the severity of the pandemic and because of the preventive and control measures such as global lockdowns, social distancing, and selfisolation that the governments imposed. Previous studies have reported significant changes in human emotions and behaviors are used to measure public sentiments about certain phenomena (such as the recent pandemic). The present study aims to study the public's sentiments during the COVID-19 outbreak based on an analytics review of public tweets highlighting changes in emotions. A dataset of 58,320 tweets extracted from Twitter and 61 academic articles was explored to analyze behavioral and emotional changes during previous and current pandemic situations. We chose the RPA – COV (Research Process Approach – COVID-19) approach, which was combined with the LBTA (Literature-Based Thematic Analysis) and the COVTA (COVID-19 Twitter Analytics). The sentiments' analysis results were coupled with word-tree analysis and highlighted that the public showed more highly neutral, positive, and mixed emotions than negative ones. The analysis pointed that people may react differently on Twitter as compared to real-life circumstances. The present study makes a significant contribution towards understanding how the public express their sentiments in pandemic situations

    Real-time processing of social media with SENTINEL: a syndromic surveillance system incorporating deep learning for health classification

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    Interest in real-time syndromic surveillance based on social media data has greatly increased in recent years. The ability to detect disease outbreaks earlier than traditional methods would be highly useful for public health officials. This paper describes a software system which is built upon recent developments in machine learning and data processing to achieve this goal. The system is built from reusable modules integrated into data processing pipelines that are easily deployable and configurable. It applies deep learning to the problem of classifying health-related tweets and is able to do so with high accuracy. It has the capability to detect illness outbreaks from Twitter data and then to build up and display information about these outbreaks, including relevant news articles, to provide situational awareness. It also provides nowcasting functionality of current disease levels from previous clinical data combined with Twitter data. The preliminary results are promising, with the system being able to detect outbreaks of influenza-like illness symptoms which could then be confirmed by existing official sources. The Nowcasting module shows that using social media data can improve prediction for multiple diseases over simply using traditional data sources

    Big Data for Qualitative Research

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    Big Data for Qualitative Research covers everything small data researchers need to know about big data, from the potentials of big data analytics to its methodological and ethical challenges. The data that we generate in everyday life is now digitally mediated, stored, and analyzed by web sites, companies, institutions, and governments. Big data is large volume, rapidly generated, digitally encoded information that is often related to other networked data, and can provide valuable evidence for study of phenomena. This book explores the potentials of qualitative methods and analysis for big data, including text mining, sentiment analysis, information and data visualization, netnography, follow-the-thing methods, mobile research methods, multimodal analysis, and rhythmanalysis. It debates new concerns about ethics, privacy, and dataveillance for big data qualitative researchers. This book is essential reading for those who do qualitative and mixed methods research, and are curious, excited, or even skeptical about big data and what it means for future research. Now is the time for researchers to understand, debate, and envisage the new possibilities and challenges of the rapidly developing and dynamic field of big data from the vantage point of the qualitative researcher

    Modeling of the spatiotemporal distribution patterns and transmission dynamics of dengue, for an early warning surveillance system

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    As doenças emergentes transmitidas por vetores representam um desafio significativo para a saúde pública global. Nos últimos tempos, os surtos de doenças como a dengue e a febre de chikungunya, aumentaram em frequência. Tal é facilitado pela globalização, pelo aumento do comércio e das viagens, e pela dispersão para novas áreas dos seus vetores invasores. Na Europa, este facto é exemplificado pela recente introdução e estabelecimento de espécies de mosquitos do género Aedes com a subsequente ocorrência de surtos de doenças como a dengue. Com a crescente disseminação da dengue em todo o mundo, a região europeia também tem vindo a registar um aumento de casos - a maioria destes relacionados com viagens. Da mesma forma, tem havido um aumento de eventos esporádicos de transmissão autóctone de dengue em áreas onde ocorre o vetor sob condições ambientais favoráveis. Assim, atualmente, a Europa enfrenta o desafio de avaliar o risco de importação de casos virémicos de dengue e a probabilidade de ocorrência de transmissão local deste vírus. Esta tese visa contribuir para a compreensão dos fatores relacionados com a importação do vírus da dengue na Europa e a sua transmissão neste território, nomeadamente na ilha da Madeira. Para tal foi implementado uma estrutura integrada de modelos computacionais da importação e transmissão da doença. A estrutura combina três submodelos: (i) um modelo explicativo de importação da doença assente em teoria de redes (ii) um modelo preditivo de aprendizagem automática e, (iii) um modelo compartimental de transmissão vetor-hospedeiro. Os modelos de teoria de redes e de aprendizagem automática foram parametrizados com recurso a dados históricos referentes a estimativas de casos importados de dengue em 21 países na Europa e índices que caracterizam parâmetros com relevância na importação da dengue: (i) tráfego de passageiros aéreos, (ii) atividade e sazonalidade da dengue, (iii) taxa de incidência, (iv) proximidade geográfica, (v) vulnerabilidade à epidemia, e, (vi) contexto económico do país de origem. O modelo compartimental de transmissão foi calibrado com parâmetros empíricos referentes ao ciclo de vida do mosquito, à transmissão viral e à variação anual de temperatura do Funchal, na ilha da Madeira. Os resultados dos modelos de teoria de redes e aprendizagem automática demonstram um maior risco de importação de casos virémicos de países com elevado tráfego de passageiros, elevadas taxas de incidência, situação económica débil e com maior proximidade geográfica em relação ao país de destino. O modelo de aprendizagem automática alcançou elevada performance preditiva, com uma pontuação AUC de 0,94. O modelo compartimental de transmissão demonstra a existência de um potencial de transmissão da dengue no Funchal nos períodos de verão e outono, com a data de chegada da pessoa infeciosa a afetar significativamente a distribuição no tempo e tamanho do pico da epidemia. Da mesma forma, a variação sazonal da temperatura afeta dramaticamente a dinâmica da epidemia, em que temperaturas iniciais mais quentes levam a surtos de maiores proporções, com o pico de casos a ocorrer mais cedo. A estrutura de modelação descrita nesta tese tem o potencial de servir como uma ferramenta integrada de vigilância de alerta precoce para a ocorrência de surtos de dengue na Europa. Este trabalho fornece orientação prática para auxiliar as autoridades de saúde pública na prevenção de surtos de dengue e na redução do risco de transmissão local, em áreas onde ocorrem os vetores. Essa estrutura, com os devidos reajustamentos, pode ser aplicada a outras doenças transmitidas por Aedes, como chikungunya e febre amarela.Emerging vector-borne diseases pose a significant global public health challenge. In recent times, outbreaks of diseases, such as dengue and chikungunya fever, have increased in frequency. This is facilitated by globalization, increase in trade and travel, and the spread of invasive vectors into new areas. In Europe, this is exemplified by the recent introduction and establishment of Aedes mosquito species and subsequent outbreaks of diseases like dengue. With the increasing spread of dengue worldwide, the European region has also experienced increase in reported cases - majority being travel related. Likewise, there has been an increase in sporadic events of autochthonous dengue transmission, in areas with established vector presence and favourable environmental conditions. Europe is currently faced with the challenge of assessing its importation risk of viraemic cases of dengue, and the probability of local transmission. This thesis aims to study the dynamics of viraemic cases importation and virus transmission of dengue fever in Europe, namely in Madeira Island. This is achieved by establishing an importation and transmission modelling framework. The framework combines three sub-models: (i) a network connectivity importation model (ii) a machine learning predictive model and, (iii) a compartmental vector-host transmission model. The network connectivity and machine learning model were both parameterized using a historical dengue importation data for 21 countries in Europe, and indices that characterize important parameters for dengue importation: (i) the air passenger traffic, (ii) dengue activity and seasonality, (iii) incidence rate, (iv) geographical proximity, (v) epidemic vulnerability, and (vi) wealth of a source country. The transmission model was calibrated using empirical parameters for the mosquito life history traits, viral transmission, and temperature seasonality of Funchal, Madeira Island. The results of the network connectivity and machine learning models demonstrate a higher importation risk of a viraemic case from source countries with high passenger traffic, high incidence rates, lower economic status, and geographical proximity to a destination country. The machine learning model achieved high predictive accuracy with an AUC score of 0.94. The transmission model demonstrates the potential for summer and autumn season transmission of dengue in Funchal, with the arrival date of the infectious person significantly affecting the distribution of the timing and peak size of the epidemic. Likewise, seasonal temperature variation dramatically affects the epidemic dynamics, with warmer starting temperatures producing large epidemics with peaks occurring more rapidly. The modelling framework described in this thesis has the potential to serve as an integrated early warning surveillance tool for dengue in Europe. This work provides practical guidance to assist public health officials in preventing outbreaks of dengue and reducing the risk of local transmission in areas with vectors presence. This framework could be applied to other Aedes-borne diseases such as chikungunya and yellow fever
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