2 research outputs found
Modeling Engagement Dynamics of Online Discussions using Relativistic Gravitational Theory
Online discussions are valuable resources to study user behaviour on a
diverse set of topics. Unlike previous studies which model a discussion in a
static manner, in the present study, we model it as a time-varying process and
solve two inter-related problems -- predict which user groups will get engaged
with an ongoing discussion, and forecast the growth rate of a discussion in
terms of the number of comments. We propose RGNet (Relativistic Gravitational
Nerwork), a novel algorithm that uses Einstein Field Equations of gravity to
model online discussions as `cloud of dust' hovering over a user spacetime
manifold, attracting users of different groups at different rates over time. We
also propose GUVec, a global user embedding method for an online discussion,
which is used by RGNet to predict temporal user engagement. RGNet leverages
different textual and network-based features to learn the dust distribution for
discussions.
We employ four baselines -- first two using LSTM architecture, third one
using Newtonian model of gravity, and fourth one using a logistic regression
adopted from a previous work on engagement prediction. Experiments on Reddit
dataset show that RGNet achieves 0.72 Micro F1 score and 6.01% average error
for temporal engagement prediction of user groups and growth rate forecasting,
respectively, outperforming all the baselines significantly. We further employ
RGNet to predict non-temporal engagement -- whether users will comment to a
given post or not. RGNet achieves 0.62 AUC for this task, outperforming
existing baseline by 8.77% AUC
Deep Exogenous and Endogenous Influence Combination for Social Chatter Intensity Prediction
Modeling user engagement dynamics on social media has compelling applications
in user-persona detection and political discourse mining. Most existing
approaches depend heavily on knowledge of the underlying user network. However,
a large number of discussions happen on platforms that either lack any reliable
social network or reveal only partially the inter-user ties (Reddit,
Stackoverflow). Many approaches require observing a discussion for some
considerable period before they can make useful predictions. In real-time
streaming scenarios, observations incur costs. Lastly, most models do not
capture complex interactions between exogenous events (such as news articles
published externally) and in-network effects (such as follow-up discussions on
Reddit) to determine engagement levels.
To address the three limitations noted above, we propose a novel framework,
ChatterNet, which, to our knowledge, is the first that can model and predict
user engagement without considering the underlying user network. Given streams
of timestamped news articles and discussions, the task is to observe the
streams for a short period leading up to a time horizon, then predict chatter:
the volume of discussions through a specified period after the horizon.
ChatterNet processes text from news and discussions using a novel time-evolving
recurrent network architecture that captures both temporal properties within
news and discussions, as well as the influence of news on discussions. We
report on extensive experiments using a two-month-long discussion corpus of
Reddit, and a contemporaneous corpus of online news articles from the Common
Crawl. ChatterNet shows considerable improvements beyond recent
state-of-the-art models of engagement prediction. Detailed studies controlling
observation and prediction windows, over 43 different subreddits, yield further
useful insights.Comment: 6 figures, 7 tables, Accepted in SIGKDD 202