Post by muntahaislam222 on Feb 28, 2024 5:12:49 GMT -6
Another post to talk about a tool that has stolen my heart: URLProfiler . If you are immersed in metrics, bloggers, influencers, or well, what do I know, perhaps simply making a list of sites where you can reach agreements, distribute content, or another cause, here we bring you a way to automate data extraction. List of Twitter users How to find the list of users, perhaps that would be for another post, but we are going to directly I am going to give the example of the profile of Nancy, an all-rounder in online marketing , from my favorite web analysts in Spain: twitter profile nansky Taking this as an example, the data to obtain would be: Number of Tweets Following Number Number of Followers Number of Lists created by each user Biography Location URL Start date on Twitter URLProfiler Custom Scraper Step 1: add profile urls to the tool. Right click and paste. twitter list urlprofiler Step 2: Click on the “Content Analysis” section, the Custom Scraper (beta) option custom scraper urlprofiler Step 3: from Chrome and the “Inspect Element” option we are going to collect the patterns we need.
At this point, we will have to do this process for each of the data, in total we have 8. Example to obtain the pattern of the number of tweets: inspect chrome element Now from the code, we go over the tweets data, and right click, we choose "Copy CSS Path". copy xpath css path We would have to paste this into URLProfiler by choosing the first field "CSS" and pasting the pattern in the last field on the right. Step 4: we fill in the pattern with each data, until we complete all the Europe Mobile Number List data we need. This is how the screen looks to me with everything well filledapply scraper Now we will click Apply and wait for it to go through the profiles that we put in the list in Step 1 and give us a CSV with all the metrics. It is important to remember the order in which we have placed the fields to extract, since the CSV simply lists them, it does not allow us to customize this field to better identify it. Step 5: Review CSV with data I give you a screenshot of the data obtained, I have frozen the first column so that you can see the relevant information that I refer to in this metrics post csv scraper urlprofiler I have added below, highlighted, what each field corresponds to, just as we said in the previous point, the syntax that follows is Data 1.
The fields that you see blank are data that does not exist in this profile, for example there are several that do not have lists created, or even do not have anything in their biography. Of metrics and influencers What is proposed in this post is a way to speed up the extraction of data that can be an important part of the deep analysis that will come later. Obviously this can be applied to many situations in social media tasks, website prospecting, competitors, link building... The objectives pursued, in each case, will determine the degree of usefulness and, above all, in which part of the analysis this is located. passed. Without a doubt, for large amounts of information it is a very quick way to obtain a first approximation of metrics of the analyzed profiles. The necessary disclaimer is that they are numerical figures that do not reflect degrees of influence or show the level of quality of the profiles studied, neither in the semantic aspect, nor in interaction. There are other ways to go deeper and that seem super necessary: Frequency with which you tweet: daily, weekly, monthly Interactions you receive and what type: RT, Fav… Last time I tweet Types of tweets.
At this point, we will have to do this process for each of the data, in total we have 8. Example to obtain the pattern of the number of tweets: inspect chrome element Now from the code, we go over the tweets data, and right click, we choose "Copy CSS Path". copy xpath css path We would have to paste this into URLProfiler by choosing the first field "CSS" and pasting the pattern in the last field on the right. Step 4: we fill in the pattern with each data, until we complete all the Europe Mobile Number List data we need. This is how the screen looks to me with everything well filledapply scraper Now we will click Apply and wait for it to go through the profiles that we put in the list in Step 1 and give us a CSV with all the metrics. It is important to remember the order in which we have placed the fields to extract, since the CSV simply lists them, it does not allow us to customize this field to better identify it. Step 5: Review CSV with data I give you a screenshot of the data obtained, I have frozen the first column so that you can see the relevant information that I refer to in this metrics post csv scraper urlprofiler I have added below, highlighted, what each field corresponds to, just as we said in the previous point, the syntax that follows is Data 1.
The fields that you see blank are data that does not exist in this profile, for example there are several that do not have lists created, or even do not have anything in their biography. Of metrics and influencers What is proposed in this post is a way to speed up the extraction of data that can be an important part of the deep analysis that will come later. Obviously this can be applied to many situations in social media tasks, website prospecting, competitors, link building... The objectives pursued, in each case, will determine the degree of usefulness and, above all, in which part of the analysis this is located. passed. Without a doubt, for large amounts of information it is a very quick way to obtain a first approximation of metrics of the analyzed profiles. The necessary disclaimer is that they are numerical figures that do not reflect degrees of influence or show the level of quality of the profiles studied, neither in the semantic aspect, nor in interaction. There are other ways to go deeper and that seem super necessary: Frequency with which you tweet: daily, weekly, monthly Interactions you receive and what type: RT, Fav… Last time I tweet Types of tweets.