Familiist (pro-natalist) communities in the social network VKontakte
<p>The database contains an upload of text comments in Russian from the social networkVkontakte in <strong>.csv format (UTF-8 encoding). </strong>Comments are collected from communities, which discuss pregnancy, childhood, motherhood, paternity, etc. The unloading contains comments under the posts with which the interaction took place. The absolute amount of likes was used as a criterion, (comments were collected where the number of likes is greater than or equal to 5). The text data was processed (stemmization and lemmatization). The data are suitable for thematic analysis (e.g. LDA - Latent Dirichlet Allocation), for modelling the graph structure of communities (the link_comment variable contains a unique identifier of the post, link_author contains a unique user identifier), for analysis of the tonalities of statements and forming a dictionary of demographic connotation. </p> <p>Sample Information:</p> <p>- Number of communities 38 </p> <p>- Content type of communities: communities in which users are mainly positive about the birth of children, motherhood, parenthood and own family are selected. But users (communities) with anti-familistic biases may be encountered. </p> <p>- Only comments with the number of likes >= 5 are collected </p> <p>- Comments are collected only from communities (the list of communities below) discussing issues related to childhood, motherhood, pregnancy, etc. </p> <p>- A sample of communities on average contains 309 thousand subscribers (maximum value - 1,482,303, minimum value - 72,570, total number of subscribers excluding intersections - 11,743 295) </p> <p>- The sample of comments contains 112,900 user comments </p> <p>Sample Structure: </p> <p>link_author - link to the author of the comment in the form of https://vk.com/*author identificator* </p> <p>gender of author (F - female, M - male, NaN - no data) </p> <p>link_comment - link to comment in the form of https://vk.com/* post identificatior on a *community wall*?reply=*comment id * </p> <p>date_time - date and time of publication (format “YYYY-MM-DD HH:MM:SS”) </p> <p>text - raw comment text </p> <p>likes - number of likes the comment has </p> <p>text_prep - processed text (punctuation marks removed, words brought down to lowercase) </p> <p>text_stem - processed text (based on the text_prep column stemmization using SnowBallstemmer (“Russian”) of the nltk library) is performed </p> <p>text_sw - processed text (based on the text_prep column stop words are deleted using word_tokenize (text) of the nltk library) </p> <p>text_lemm - processed text (lemmatization using mystem.lemmatize (text) of pymystem3 library is performed based on the text_prep column) </p> <p>List of communities (38 communities):</p> <p>https://vk.com/club52388302</p> <p>https://vk.com/club34677924</p> <p>https://vk.com/club99834596</p> <p>https://vk.com/club170234932</p> <p>https://vk.com/club20199180</p> <p>https://vk.com/club118030893</p> <p>https://vk.com/club14395935</p> <p>https://vk.com/club100104267</p> <p>https://vk.com/club181526404</p> <p>https://vk.com/club35095382</p> <p>https://vk.com/club58530763</p> <p>https://vk.com/club69716165</p> <p>https://vk.com/club29746763</p> <p>https://vk.com/club78865067</p> <p>https://vk.com/club20709572</p> <p>https://vk.com/club93466205</p> <p>https://vk.com/club61700163</p> <p>https://vk.com/club91423062</p> <p>https://vk.com/club69285929</p> <p>https://vk.com/club104012302</p> <p>https://vk.com/club20622108</p> <p>https://vk.com/club86333616</p> <p>https://vk.com/club24765</p> <p>https://vk.com/club87169444</p> <p>https://vk.com/club86688308</p> <p>https://vk.com/club93776129</p> <p>https://vk.com/club47207301</p> <p>https://vk.com/club39873171</p> <p>https://vk.com/club59224150</p> <p>https://vk.com/club7430494</p> <p>https://vk.com/club37739956</p> <p>https://vk.com/club59701255</p> <p>https://vk.com/club27427277</p> <p>https://vk.com/club126238531</p> <p>https://vk.com/club127678644</p> <p>https://vk.com/club57782234</p> <p>https://vk.com/club51314884</p> <p>https://vk.com/club134261249</p>
ShareScore
32/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 8
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 0
- Engagement
- 4
Topics
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