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transidentite_​memoire2023-otti.e.knar

Challenge-Team11

This workflow is designed to understand the importance of community on tweets about NFTs.

1. First step of analytics : We want to know what kind of topics are in those tweets related to transidentité inorder to formulate our hypotheses for text mining.We can have a first overview of the type of used vocabulary in order to understanthow transidentités are mentioned and discussed. 2. Second step of analytics :According to the topics found, we decided to formulate our hypotheses :-Around community and time/active vocab in tweets-Around nft community vocab in user descriptions to see if the user who (re)tweets belongs to the community deeply Data preparation :Here we extract our data and create documentsfor all following operations. WORFLOW'S AIM :The aim of this workflow is to analysetweets about transidentité. 2. 1 Hypothese 1 + hashtag mining on tweets :H1= tweets containing community and reactiveness/time vocabulary tend toimpact positively the number of retweets concerning transidentité 2. 2 Hypothese 2 :H2= when the bios of users are mentionning queer community, the user ismore likely to retweet about transidentité in a positive way 2. 3 Hypothese 3 :H3= when users bios are related to queer community they are more likely to usecommunity&time vocabulary and therefore impact positively retweets abouttransidentité + hashtag miningquery : transidentitéfor tweets and for user descriptions Exploratory analysis: topic models Explanatory analytics : textmining on tweets for h1 Explanatory analytics : textmining on user descriptions for h2 Table Reader Document creations Twitter API stepsto extract our data Explanatory analytics : text miningon tweets w/ specific users for h3 1. First step of analytics : We want to know what kind of topics are in those tweets related to transidentité inorder to formulate our hypotheses for text mining.We can have a first overview of the type of used vocabulary in order to understanthow transidentités are mentioned and discussed. 2. Second step of analytics :According to the topics found, we decided to formulate our hypotheses :-Around community and time/active vocab in tweets-Around nft community vocab in user descriptions to see if the user who (re)tweets belongs to the community deeply Data preparation :Here we extract our data and create documentsfor all following operations. WORFLOW'S AIM :The aim of this workflow is to analysetweets about transidentité. 2. 1 Hypothese 1 + hashtag mining on tweets :H1= tweets containing community and reactiveness/time vocabulary tend toimpact positively the number of retweets concerning transidentité 2. 2 Hypothese 2 :H2= when the bios of users are mentionning queer community, the user ismore likely to retweet about transidentité in a positive way 2. 3 Hypothese 3 :H3= when users bios are related to queer community they are more likely to usecommunity&time vocabulary and therefore impact positively retweets abouttransidentité + hashtag miningquery : transidentitéfor tweets and for user descriptions Exploratory analysis: topic models Explanatory analytics : textmining on tweets for h1 Explanatory analytics : textmining on user descriptions for h2 Table Reader Document creations Twitter API stepsto extract our data Explanatory analytics : text miningon tweets w/ specific users for h3

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Created by: knime
Created at: 2014-09-11
On NodePit since: 2023-05-07
Last update: 2024-04-27
Created with KNIME version: v4.4.1

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