Network Models and Content Models for Social Media Users, Domains of Interest, and User Similarity
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Dataset containing processed Twitter users information for content and graph analysis. Users are grouped in 4 manually-defined communities (fashion, finance, Australian writers and chess players) and an additional set of 160 random users is included in the collection.Content features are calculated with NLTK and Dandelion: each user is represented by a vector of words and entities occurrences, based on their tweets.Networks are built using SNAP, network features are extracted using node2vec: for each domain, the following, followers and mentions graph is calculated and stored in two separated files for edges and nodes; additionally, the embeddings are stored for each node in a separated file' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["embeddings","graphs","text"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Dataset containing processed Twitter users information for content and graph analysis. Users are grouped in 4 manually-defined communities (fashion, finance, Australian writers and chess players) and an additional set of 160 random users is included in the collection.Content features are calculated with NLTK and Dandelion: each user is represented by a vector of words and entities occurrences, based on their tweets.Networks are built using SNAP, network features are extracted using node2vec: for each domain, the following, followers and mentions graph is calculated and stored in two separated files for edges and nodes; additionally, the embeddings are stored for each node in a separated file' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Dataset containing processed Twitter users information for content and graph analysis. Users are grouped in 4 manually-defined communities (fashion, finance, Australian writers and chess players) and an additional set of 160 random users is included in the collection.Content features are calculated with NLTK and Dandelion: each user is represented by a vector of words and entities occurrences, based on their tweets.Networks are built using SNAP, network features are extracted using node2vec: for each domain, the following, followers and mentions graph is calculated and stored in two separated files for edges and nodes; additionally, the embeddings are stored for each node in a separated file
embeddings; graphs; text