Copyright (C) Microsoft Research

This copy of data/models is to be used for research purposes only. Do not redistribute the data/models.

Contacts: Svitlana Volkova, Johns Hopkins University
Center for Language and Speech Processing (svitlana@jhu.edu)
=========================================================================
Predicting psycho-demographics and emotions in Twitter citations:
 - Inferring Latent User Properties from Texts Published in Social Media  
   Svitlana Volkova, Yoram Bachrach, Michael Armstrong and Vijay Sharma
   In Proc. of Twenty-Ninth Conference on Artificial Intelligence (AAAI 2015). 

Demo link: http://twitterpredictor.cloudapp.net (pswd: twitpredMSR2014)
=========================================================================

DESCRIPTION
The dataset contains userIDs (and their ~200 tweetIDs collected over the summer of 2014 - userIDsToTweetIDs) annotated with 15 psycho-demographic attributes described below (UserIDsToAttributes).

================relationship_status====================
{'Divorced': 30, 'In a relationship': 1015, 'Married': 368, 'Other (not listed)': 283, 'Single': 3302}

================iq/intelligence=================
{'Above average': 863, 'Average': 2941, 'Below average': 989, 'Much above average': 48, 'Much below average': 157}

================gender==========================
{'Female': 2874, Male: 2124}

====================age=========================
{'18':355, '19':300, '20':534, '21':318, '22':425, '23':279, '24':300, '25':497, '26':182, '27':130, '28':204, '29':77, '30':282, '31':37, '32':95, '33':47, '34':36, '35':143, '36':27, '37':21, '38':29, '39':10, '40':73, '41':3, '42':17, '43':4, '44':3, '45':49, '46':3, '47':3, '48':7,'49':1, '50':26, '51':3, '52':3, '53':1, '54':2, '55':16, '56':2, '57':1, '58':3, '59':1, '60':3, '61':1, '62':1, '64':1, '65':1, '67':1, '68':1, '69':1, '70':2}

================political=======================
{'Conservative': 595, 'Independent': 432, 'Liberal': 1903, 'Unaffiliated': 2070}

================optimism========================
{'Extreme optimist': 378, 'Extreme pessimist': 94, Neither Optimist Nor Pessimist': 1438,  'Optimist': 2277, 'Pessimist': 813}

===============children=========================
{'No': 4203, 'Yes': 797}

================religion========================
{'Christian': 2789, 'Hindu': 40, 'Jewish': 51, 'Muslim': 73, 'Other': 435, 'Unaffiliated': 1610}

================race/ethnicity==================
{'Asian': 160, 'Black': 1705, 'Hispanic': 519, 'Indian': 61, 'Other': 146, 'White': 2409}

================income==========================
{'Between $35': 1442, 'Over $75': 233, 'Under $35': 3324}

================life_satisfaction===============
{'Dissatisfied': 746, 'Neither Nor': 1211, 'Satisfied': 2498,  'Very dissatisfied': 94, 'Very satisfied': 451,}

================narcissism======================
{'Agree':1439, 'Agree strongly':437, 'Disagree':1723, 'Disagree strongly':374, 'Neither agree nor disagree':988}

================anxious_moody_stressed==========
{'Agree':919, 'Agree strongly':225, 'Disagree':2020, 'Disagree strongly':739, 'Neither agree nor disagree':1057}

================education=======================
{'Bachelor's Degree': 1414, 'Graduate Degree': 161, 'High School': 3423}

================excited_enthusiastic============
{'Agree':2001, 'Agree strongly':607, 'Disagree':556, 'Disagree strongly':102, 'Neither agree nor disagree':904}

N/A values stand for not available.