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国社交媒体对网络媒体（cybermedia）网络流量的量化影响分析》所收集的通用数据。本研究提出四项核心研究问题：欧盟范围内网络媒体产生的总网络流量中，来自社交媒体的占比为多少？该占比相较于直接访问流量及通过搜索引擎优化（Search Engine Optimization，SEO）定位带来的流量，孰高孰低？哪些社交媒体对网络流量的影响更为显著？此外，网络媒体的社交媒体流量占比，与用户平均访问时长、单访问页面数，以及学界定义为“仅浏览页面内容未发生任何交互行为”的跳出率（bounce rate），是否存在关联？ 为解答上述问题，研究团队首先筛选了2020年12月31日英国脱欧后，当前欧盟27国中网络流量最高的网络媒体。针对每个国家，我们结合流量统计工具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/）提供的全球网络流量指标，各选取5家网络媒体。未采用各国本地流量指标，原因在于前两款工具的统计结果已具备足够显著性，且本研究的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权重。 所有入选媒体均归属新闻机构，排除隶属于电信门户网站或服务提供商的平台；部分为传统资讯企业（含报纸与电视台），其余则为原生数字媒体（digital natives），该差异不影响本研究的研究性质。 随后，研究团队对上述网络媒体的网络流量数据进行分析。选取的时间周期为2021年10月至12月，以及2022年1月至3月，共计六个月。我们认为该周期可抵消单月偶发波动，提升所得数据的精准性。 本研究采用SimilarWeb工具获取上述数据，该工具为当前分析门户网站网络流量的主流高精度工具，但其仅统计桌面端与笔记本电脑端的流量，未涵盖移动设备流量——目前市场上尚无成熟工具可完成移动设备流量的精准测算。 本数据集包含以下内容： 1. 网络流量通用数据：用户平均访问时长、单访问页面数与跳出率 2. 分国家的网络流量来源数据 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分布情况 5. 社交媒体来源流量与直接访问流量、搜索引擎来源流量的对比数据