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Coverage and Evolution of Cancer and Its Risk Factors - A Quantitative Study with Social Signals and Web-Data

机译:癌症的覆盖范围及其风险因素 - 具有社会信号和网络数据的定量研究

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In today's world of digitization, information is increasing at an exponential rate due to the ubiquitous nature of social media and web sources. With the advancement in Social Signal Processing, Social Informatics & Computing, and big data mining techniques, we can use this information in the health domain to determine the trend in the spread of symptoms and risk factors of diseases temporally. In this paper, our primary focus is to study coverage and evolution of risk factors of 19 most common Cancers in the last ten years (2009 2018) from the pieces of evidence collected from Tweets (Social Signal) and Wikipedia (Web Data) and demonstrate the relevant findings. Moreover, temporal variations are also shown in coverage of each cancer and risk factors to determine their evolution over time. We try to find the answers of questions like, Do different information sources show different risk factors for the same cancer ? or Is there any visible and distinctive trend in the temporal variation of any specific cancer or its risk factors? Each of the analysis unfolds various exciting observations. The most important among these is that some risk factors have the most adverse effects according to the one information source, but they are not at all harmful according to the other. Finally, the correlation of information sources, i.e., social signals and web data, is shown concerning variation in coverage of cancers and risk factors.
机译:在当今的数字化世界中,由于社交媒体和网络来源的无处不在的性质,信息正在增加。随着社会信号处理,社会信息学和计算的进步和大数据挖掘技术,我们可以在健康领域中使用这些信息来确定症状传播和疾病风险因素的趋势。在本文中,我们的主要重点是在过去十年(2018年)从推文(社会信号)和维基百科(Web数据)中收集的证据中研究了19岁以下最常见的癌症的危险因素的覆盖率和演变。相关调查结果。此外,在每个癌症的覆盖范围和危险因素的覆盖范围内也显示了时间变化,以确定它们随时间的进化。我们试图找到问题的答案,如不同的信息来源,表明同一癌症的不同风险因素?或者在任何特定癌症的时间变异或其风险因素的时间变化中有任何可见和独特的趋势吗?每个分析展开了各种令人兴奋的观察。其中最重要的是,根据一个信息来源,一些风险因素具有最不利的影响,但它们并不符合另一个信息来源。最后,示出了信息源,即社会信号和Web数据的相关性关于癌症覆盖范围和风险因素的变化。

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