A Deep Learning and Knowledge Transfer Based Architecture for Social Media User Characteristic Determination

By Matthew Riemer, Sophia Krasikov, Harini Srinivasan. 2015

13 citations recorded by the source corpus.

Determining explicit user characteristics based on interactions on Social Media is a crucial task in developing recommendation and social polling solutions. For this purpose, rule based and N-gram based techniques have been proposed to develop user profiles, but they are only fit for detecting user attributes that can be classified by a relatively simple logic or rely on the presence of a large amount of training data. In this paper, we propose a general purpose, end-to-end architecture for text analytics, and demonstrate its effectiveness for analytics based on tweets with a relatively small training set. By performing unsupervised feature learning and deep learning over labeled and unlabeled tweets, we are able to learn in a more generalizable way than N-gram techniques. Our proposed hidden layer sharing approach makes it possible to efficiently transfer knowledge between related NLP tasks. This approach is extensible, and can learn even more from metadata available about Social Media users. For the task of user age prediction over a relatively small corpus, we demonstrate 38.3% error reduction over single task baselines, a total of 44.7% error reduction with the incorporation of two related tasks, and achieve 90.1% accuracy when useful metadata is present.

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Citations in the corpus

Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference (2018, background): “In this section we would like to generalize our interpretation of a large set of different weight sharing schemes including (Riemer et al., 2015; Bengio et al., 2015; Rosenbaum et al., 2018; Serrà et al., 2018) and how the concept of weight sharing impacts the dynamics of transfer (equation 1) and…”
Representation Stability as a Regularizer for Improved Text Analytics Transfer Learning (2017, neutral): “Knowledge transfer from multiple tasks was considered to estimate the age of Twitter users based on the content of their tweets in (Riemer et al., 2015).”
Learning Abstract Options (2018, background): “This a somewhat arbitrary choice of the extent of weight sharing in light of recent work that focuses on more dynamic sharing patterns in multi-task learning, lifelong learning, and continual learning settings [29, 23, 32, 34, 6, 33, 10, 17, 30, 31].”
On the Role of Weight Sharing During Deep Option Learning (2019, methodology): “We also follow the option value function upon arrival U from Riemer et al. (2018). As we are focusing on the full active option hierarchy, we do not need the term for only”