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Social Media Use is Predictable from App Sequences: Using LSTM and Transformer Neural Networks to Model Habitual Behavior
Additionally, our analyses reveal that the person-level predictability of social media use is not substantially related to the frequency of smartphone use in general or the frequency of social media use, indicating that our approach captures an aspect of habits that is distinct from behavioral frequency.
Context-Aware Prediction of User Engagement on Online Social Platforms
The success of online social platforms hinges on their ability to predict and understand user behavior at scale.
Generalizable Error Modeling for Search Relevance Data Annotation Tasks
This paper presents a predictive error model trained to detect potential errors in search relevance annotation tasks for three industry-scale ML applications (music streaming, video streaming, and mobile apps) and assesses its potential to enhance the quality and efficiency of the data annotation process.
Model Share AI: An Integrated Toolkit for Collaborative Machine Learning Model Development, Provenance Tracking, and Deployment in Python
AIMS features collaborative project spaces and a standardized model evaluation process that ranks model submissions based on their performance on unseen evaluation data, enabling collaborative model development and crowd-sourcing.
Large Language Models Can Infer Psychological Dispositions of Social Media Users
As Large Language Models (LLMs) demonstrate increasingly human-like abilities in various natural language processing (NLP) tasks that are bound to become integral to personalized technologies, understanding their capabilities and inherent biases is crucial.
