ArticlePloS one2026
Incorporating topical stance into signed bipartite networks for user retweet prediction.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Social networks accelerate information dissemination, and retweet behavior is an important way of user interaction. User retweet prediction analyzes user characteristics to predict retweet behavior and emotional polarity, which can help platforms understand user emotional tendencies and can be applied to scenarios such as public opinion analysis. However,most existing studies on tree like propagation chains focus on link existence and rarely combine positive negative polarity with topic semantics. This paper constructs a signed bipartite network using user and topic nodes, proposes a Topic Node weighting-based Topical Stance Representation (TNTSR) method, and develops a Topic Stance-integrated Graph Attention Neural Network (TSGAT) for retweet prediction. Experiments on social platform datasets show that both methods outperform benchmarks like SGCN in predicting retweet behavior and polarity. This research effectively leverages topic stance and network structure, enhancing the accuracy of user retweet prediction.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.