When Silver Is As Good As Gold: Using Weak Supervision to Train Machine Learning Models on Social Media Data

Over the last decade, advances in machine learning have led to an exponential growth in artificial intelligence i.e., machine learning models capable of learning from vast amounts of data to perform several tasks such as text classification, regression, machine translation, speech recognition, and many others. While massive volumes of data are available, due to the manual curation process involved in the generation of training datasets, only a percentage of the data is used to train machine learning models. The process of labeling data with a ground-truth value is extremely tedious, expensive, and is the major bottleneck of supervised learning. To curtail this, the theory of noisy learning can be employed where data labeled through heuristics, knowledge bases and weak classifiers can be utilized for training, instead of data obtained through manual annotation. The assumption here is that a large volume of training data, which contains noise and acquired through an automated process, can compensate for the lack of manual labels. In this study, we utilize heuristic based approaches to create noisy silver standard datasets. We extensively tested the theory of noisy learning on four different applications by training several machine learning models using the silver standard dataset with several sample sizes and class imbalances and tested the performance using a gold standard dataset. Our evaluations on the four applications indicate the success of silver standard datasets in identifying a gold standard dataset. We conclude the study with evidence that noisy social media data can be utilized for weak supervisio

Leveraging Large Language Models and Weak Supervision for Social Media data annotation: an evaluation using COVID-19 self-reported vaccination tweets

The COVID-19 pandemic has presented significant challenges to the healthcare industry and society as a whole. With the rapid development of COVID-19 vaccines, social media platforms have become a popular medium for discussions on vaccine-related topics. Identifying vaccine-related tweets and analyzing them can provide valuable insights for public health research-ers and policymakers. However, manual annotation of a large number of tweets is time-consuming and expensive. In this study, we evaluate the usage of Large Language Models, in this case GPT-4 (March 23 version), and weak supervision, to identify COVID-19 vaccine-related tweets, with the purpose of comparing performance against human annotators. We leveraged a manu-ally curated gold-standard dataset and used GPT-4 to provide labels without any additional fine-tuning or instructing, in a single-shot mode (no additional prompting)

A hole-filling algorithm for triangular meshes

technical reportData obtained by scanning 3D models typically contains missing pieces and holes. These can be caused due to scanning artifacts or artifacts in the surface due to wear and tear. We provide a method based on the Moving Least Squares projection to fill holes in triangular meshes obtained during the process of surface reconstruction . Our method can be applied to holes with non-planar geometry as well as small, planar holes