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Found 7 papers, 2 papers with code
Building for Tomorrow: Assessing the Temporal Persistence of Text Classifiers
Therefore an ability to predict a model's ability to persist over time can help design models that can be effectively used over a longer period of time.
Capturing Stance Dynamics in Social Media: Open Challenges and Research Directions
Social media platforms provide a goldmine for mining public opinion on issues of wide societal interest and impact.
The emojification of sentiment on social media: Collection and analysis of a longitudinal Twitter sentiment dataset
There is however a gap in the longitudinal study of how sentiment evolved in social media over the years.
Opinions are Made to be Changed: Temporally Adaptive Stance Classification
We propose a novel approach to mitigate this performance drop, which is based on temporal adaptation of the word embeddings used for training the stance classifier.
QMUL-SDS @ DIACR-Ita: Evaluating Unsupervised Diachronic Lexical Semantics Classification in Italian
In this paper, we present the results and main findings of our system for the DIACR-ITA 2020 Task.
QMUL-SDS @ SardiStance: Leveraging Network Interactions to Boost Performance on Stance Detection using Knowledge Graphs
This paper presents our submission to the SardiStance 2020 shared task, describing the architecture used for Task A and Task B.
QMUL-SDS at CheckThat! 2020: Determining COVID-19 Tweet Check-Worthiness Using an Enhanced CT-BERT with Numeric Expressions
The purpose of this task is to determine the check-worthiness of tweets about COVID-19 to identify and prioritise tweets that need fact-checking.
