{'fraction_non_alphanumeric': 0.01180327868852459, 'fraction_numerical': 0.0013114754098360656, 'mean_word_length': 5.055555555555555, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Temporal Word Embeddings for Dynamic User Profiling in Twitter

The research described in this paper focused on exploring 
the domain of user profiling, a nascent and contentious technology which 
has been steadily attracting increased interest from the research community as its potential for providing personalised digital services is realised. 
An extensive review of related literature revealed that limited research 
has been conducted into how temporal aspects of users can be captured 
using user profiling techniques. This, coupled with the notable lack of 
research into the use of word embedding techniques to capture temporal 
variances in language, revealed an opportunity to extend the Random Indexing word embedding technique such that the interests of users could 
be modelled based on their use of language. To achieve this, this work 
concerned itself with extending an existing implementation of Temporal 
Random Indexing to model Twitter users across multiple granularities of 
time based on their use of language. The product of this is a novel technique for temporal user profiling, where a set of vectors is used to describe 
the evolution of a Twitter user’s interests over time through their use of 
language. The vectors produced were evaluated against a temporal implementation of another state-of-the-art word embedding technique, the 
Word2Vec Dynamic Independent Skip-gram model, where it was found 
that Temporal Random Indexing outperformed Word2Vec in the generation of temporal user profiles.