Facebook parser
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Post a project like this$10.0k
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Software Engineer | Full Stack Developer | Cloud Architect | Motion Graphics Animator
London
UK based | Growth Marketing | Social Media | PPC | PR | SEO | Digital Marketing | Online | Inbound | Email Marketing | Lead Generation | Web Development | Sales | Project Management | AI
Preston
WordPress , Shopify, Wix, Django, Laravel, PHP, .Net, Java, React JS, MYSQL | Social Media Manager | ML & AI Engineer | Mobile Application Developer | Graphics Designer | Electrical Engineer
Albany
24487638461932473367823522284710985061395078611177122175069519717232060301
Description
Experience Level: Expert
Purpose: parsing all Facebook.
Realization:
Facebook has rigid limits on almost all parsing, except parsing on type a name + the city.
We will also use it.
We specify the list by the city and the list of names. We parse everything that we are given out.
The algorithm will be approximately such:
There are cities of New York, Philadelphia
names: Mindi, John, Alice
The parser processes so:
Mindi + New York, John + New York, Alice + New York
Mindi + Philadelphia, John + Philadelphia, Alice + Philadelphia
It is required about 1000 streams. Possibly to use Amazon (as I understand, they for these purposes have servers)
ipv6 proxy.
Possibly, via the browser or his imitation. Though it is possible and through хттп to try inquiries.
Accounts, anyway, will fly to the ban so the registrar which will tighten accounts for the period of parsing is necessary.
Further all this is going to base. In which we can:
1) To use minus word on categories necessary to us. (for example, name and word "Lera". All people with a name of Ler, will leave)
2) Plus words on categories necessary to us. For example, name and word "Lera". All people with a name of Ler, will remain.
Filtration stage multistage. I.e. operations plus and minus of words can be used several times. As in a usual DB.
And also the rating on the most popular coincidence is required. I will give an example. We have a category Name. Software considers how many names coincide and build rating from the most large number to the most smaller.
If at us in the List:
Sergey Filatov
Sergey Yesenin
Alexander Pushkin
Software will build everything so:
Sergey - 2
Alexander 1
Categories:
1)! All! column Favorites (rating)
2) Education
3) City
4) name and surname
5) Favourite quotes (rating _)
6) Work (rating)
7) floor
Realization:
Facebook has rigid limits on almost all parsing, except parsing on type a name + the city.
We will also use it.
We specify the list by the city and the list of names. We parse everything that we are given out.
The algorithm will be approximately such:
There are cities of New York, Philadelphia
names: Mindi, John, Alice
The parser processes so:
Mindi + New York, John + New York, Alice + New York
Mindi + Philadelphia, John + Philadelphia, Alice + Philadelphia
It is required about 1000 streams. Possibly to use Amazon (as I understand, they for these purposes have servers)
ipv6 proxy.
Possibly, via the browser or his imitation. Though it is possible and through хттп to try inquiries.
Accounts, anyway, will fly to the ban so the registrar which will tighten accounts for the period of parsing is necessary.
Further all this is going to base. In which we can:
1) To use minus word on categories necessary to us. (for example, name and word "Lera". All people with a name of Ler, will leave)
2) Plus words on categories necessary to us. For example, name and word "Lera". All people with a name of Ler, will remain.
Filtration stage multistage. I.e. operations plus and minus of words can be used several times. As in a usual DB.
And also the rating on the most popular coincidence is required. I will give an example. We have a category Name. Software considers how many names coincide and build rating from the most large number to the most smaller.
If at us in the List:
Sergey Filatov
Sergey Yesenin
Alexander Pushkin
Software will build everything so:
Sergey - 2
Alexander 1
Categories:
1)! All! column Favorites (rating)
2) Education
3) City
4) name and surname
5) Favourite quotes (rating _)
6) Work (rating)
7) floor
Oleg N.
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Last project
6 May 2024
Russian Federation
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