In [3]:
import os
import pandas as pd
On first week of the social media scraping tooltrack we used Netvizz.
It has six different modules
We received a couple of files for each page. For example:
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ethospagedatadir = "page_126517697403099_2017_10_31_14_48_28"
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os.listdir(ethospagedatadir)
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And we explored the shape and nature of the data, to see what did we receive from Netvizz.
The .gdf graph file data has nodes
In [67]:
ethosg = {'nodes': pd.read_csv(ethospagedatadir + '/' + 'page_126517697403099_2017_10_31_14_48_28.gdf', nrows=416),
'edges': pd.read_csv(ethospagedatadir + '/' + 'page_126517697403099_2017_10_31_14_48_28.gdf', skiprows=417)
}
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ethosg['nodes'].sample(3)
Out[68]:
and edges in the same file
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ethosg['edges'].sample(3)
Out[63]:
And the data in the fullstats data frame looks like this.
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ethosstats = pd.read_csv(ethospagedatadir + '/' + 'page_126517697403099_2017_10_31_14_48_28_fullstats.tab', sep='\t')
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ethosstats.sample(3)
Out[65]:
And the data in the comments data frame looks like this.
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ethoscomments = pd.read_csv(ethospagedatadir + '/' + 'page_126517697403099_2017_10_31_14_48_28_comments.tab', sep='\t')
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ethoscomments.sample(3)
Out[66]:
Netvizz is described in Rieder's paper Studying Facebook via Data Extraction: The Netvizz Application (WebSci'13).
We can use Tableau, Gephi or other tools to merge and/or manipulate the data, and Table 2 Net to define other graphs.
The point was to be able to bring these possibilities to our project groups, and raise some questions.