Analysis of the Quantitative Impact of Social Networks General Data.doc

General data recollected for the studio " Analysis of the Quantitative Impact of Social Networks on Web Traffic of Cybermedia in the 27 Countries of the European Union". Four research questions are posed: what percentage of the total web traffic generated by cybermedia in the European Union comes from social networks? Is said percentage higher or lower than that provided through direct traffic and through the use of search engines via SEO positioning? Which social networks have a greater impact? And is there any degree of relationship between the specific weight of social networks in the web traffic of a cybermedia and circumstances such as the average duration of the user's visit, the number of page views or the bounce rate understood in its formal aspect of not performing any kind of interaction on the visited page beyond reading its content? To answer these questions, we have first proceeded to a selection of the cybermedia with the highest web traffic of the 27 countries that are currently part of the European Union after the United Kingdom left on December 31, 2020. In each nation we have selected five media using a combination of the global web traffic metrics provided by the tools Alexa (https://www.alexa.com/), which ceased to be operational on May 1, 2022, and SimilarWeb (https:// www.similarweb.com/). We have not used local metrics by country since the results obtained with these first two tools were sufficiently significant and our objective is not to establish a ranking of cybermedia by nation but to examine the relevance of social networks in their web traffic. In all cases, cybermedia whose property corresponds to a journalistic company have been selected, ruling out those belonging to telecommunications portals or service providers; in some cases they correspond to classic information companies (both newspapers and televisions) while in others they refer to digital natives, without this circumstance affecting the nature of the research proposed. Below we have proceeded to examine the web traffic data of said cybermedia. The period corresponding to the months of October, November and December 2021 and January, February and March 2022 has been selected. We believe that this six-month stretch allows possible one-time variations to be overcome for a month, reinforcing the precision of the data obtained. To secure this data, we have used the SimilarWeb tool, currently the most precise tool that exists when examining the web traffic of a portal, although it is limited to that coming from desktops and laptops, without taking into account those that come from mobile devices, currently impossible to determine with existing measurement tools on the market. It includes: Web traffic general data: average visit duration, pages per visit and bounce rate Web traffic origin by country Percentage of traffic generated from social media over total web traffic Distribution of web traffic generated from social networks Comparison of web traffic generated from social netwoks with direct and search procedures本数据集为研究课题《欧盟27国社交媒体（social networks）对网络媒体（cybermedia）网络流量的量化影响分析》所收集的通用研究数据。本研究提出四项核心研究问题：其一，欧盟范围内网络媒体产生的总网络流量中，有多大比例源自社交媒体？其二，该占比相较于直接访问流量，以及通过搜索引擎优化（Search Engine Optimization，SEO）定位获取的搜索流量孰高孰低？其三，哪些社交媒体的流量影响程度更为显著？其四，网络媒体的网络流量中社交媒体的权重占比，与用户平均访问时长、页面浏览量，以及用户仅浏览页面内容而未产生任何交互行为的跳出率（bounce rate）等指标间是否存在相关关系？ 为解答上述问题，研究团队首先从2020年12月31日英国脱欧后的欧盟27个成员国中，筛选网络流量排名靠前的网络媒体。在每个国家内，结合全球网络流量统计工具Alexa（已于2022年5月1日停止运营，官网：https://www.alexa.com/）与SimilarWeb（官网：https://www.similarweb.com/）提供的全球流量指标，共选取5家媒体。研究未采用各国本地流量统计指标，原因在于前述两款工具的统计结果已具备足够显著性，且本研究的核心目标并非对各国网络媒体进行排名，而是探究社交媒体在其网络流量中的相关性。 本次入选的网络媒体均归属新闻机构，排除了隶属于电信门户或服务提供商的平台；部分媒体为传统新闻企业（涵盖报纸与电视台），其余则为原生数字媒体，该属性差异不影响本研究的既定研究方向。 随后研究团队对上述入选网络媒体的网络流量数据展开分析。选取的统计时段为2021年10月、11月、12月及2022年1月、2月、3月，共计6个月。研究团队认为，该6个月的周期可有效抵消单月偶发波动，提升所得数据的精准性。 为获取该类流量数据，研究团队使用了SimilarWeb工具——当前市面上用于分析门户网站网络流量的主流精准工具之一，但该工具仅统计来自台式机与笔记本电脑的流量，未纳入移动设备流量，而现有市场测量工具目前尚无法实现移动设备流量的精准测定。 本数据集包含以下内容： 1. 网络流量通用指标数据：平均访问时长、单次访问页面数、跳出率 2. 分国家的网络流量来源分布数据 3. 社交媒体流量占总网络流量的比例 4. 社交媒体来源网络流量的结构分布 5. 社交媒体来源流量与直接访问流量、搜索流量的对比数据