画出这个故事,告诉他们紧急情况。来自该地区的数字声音

Domenico Trezza, Gabriella Punziano, Ciro Clemente De Falco
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引用次数: 1

摘要

与COVID-19大流行的第一阶段(2020年3月至6月)有关,最近一些关于应用地理定位推文内容分析的研究(Bashar et alii 2020, Punziano et alii 2020)表明,冠状病毒传播(意大利北部受影响最大)与关于大流行的社会叙事极性之间存在负相关关系。简言之,受影响最严重地区的"复原"社会叙述与受影响较轻地区的消极和忧虑的紧急叙述相对应。就流行病学数据而言,大流行的第二阶段(也称为2020年秋季的"第二波")与第一阶段(ISSa 2020)有很大不同。在不考虑第一波数据(Istat 2020a)可靠性问题的情况下,紧急情况的严重程度在意大利各地更为相关和均匀。该研究质疑传染的地理位置与叙事的地理位置之间是否存在差异。鉴于病毒的传播越来越同质化,人们一直认为,数字领域最终也呈现出一种更加统一的负面情绪叙事。这个问题是通过分析地理定位的推文语料库来解决的,这些推文是从10月新的封锁到2020年圣诞节前部分和碎片化的重新开放期间提取的。应用文本挖掘和GIS分析相结合的模型,绘制了Twitter上社会话语中最常出现的主题。将新出现的社会叙事(COVID-Issues)的地理位置与传染传播(COVID-Spread)的地理位置和规范(COVID-Measures)进行比较,可以在COVID-19第二次出现期间发现这三个维度之间关系的趋势。
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Mappare il racconto, raccontare l’emergenza. Voci digitali dai territori
Related to the first period of the COVID-19 pandemic (March-June 2020), some recent studies on the application of content analysis of geolocalized tweets (Bashar et alii 2020, Punziano et alii 2020) have demonstrated the negative relation between the Coronavirus spread (with Northern Italy most affected) and the polarity of social narratives about the pandemic. In brief, the «resilient» social narrative of the most impacted regions has corresponded to a negative and worried emergency narrative of the less affected regions. In relation to epidemiological data, the second phase of the pandemic (also referred to as the «second wave», in autumn 2020) has been very different from the first (ISSa 2020). The severity of emergency, without considering questions about the reliability of the first wave data (Istat 2020a), has been more relevant and homogeneous across Italy. The study questions whether there are differences between the geography of contagion and that of the narrative. Given the increasingly homogeneous spread of the virus, the assumption has been that the digital arena also has ended up showing a narrative more united on negative sentiments. The issue is addressed by analyzing a corpus of geolocalized tweets, extracted in the period from the new October lockdown to the partial and fragmented pre-Christmas reopenings in 2020. Following the application of a model combining text mining and GIS analysis, the most recurrent themes in social discourse on Twitter were mapped. This geography of emerging social narratives (COVID-Issues) compared with the geography of contagion spread (COVID-Spread) and the norms (COVID-Measures) allowed to detect the trend in the relationship of these three dimensions during the second emergence from COVID-19.
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