Mapping sentiment dependencies among online influencer communities on Weibo

Abstract Influencer-centered communities on social media are connected through overlapping audiences and shared topical attention, yet their directed sentiment dependencies remain difficult to observe directly. We reconstruct inter-community influence networks from sentiment time series on Weibo, a major Chinese microblogging platform. We track several prominent technology bloggers over six months, aggregating comment-level sentiment into collective sentiment trajectories for each blogger’s commenting audience. These audiences are treated as macro-agents, an aggregation justified by shared content exposure and homophily-driven sentiment alignment. Using opinion dynamics models as network inference tools, we estimate directed influence graphs capturing how collective sentiment in one community shapes that in others. The inferred networks exhibit pronounced sparsity and clear structural asymmetry. Some communities act as hubs whose sentiment radiates outward, while others are largely internally driven, reacting to their blogger’s posts and weakly coupled to peer communities. These patterns remain consistent across model variants, and the learned models demonstrate stable two-period-ahead predictive performance on held-out data. Our findings show that collective online sentiment is well described by structured inter-community averaging, yielding interpretable, empirically grounded influence graphs. The proposed framework provides an interpretable approach for mapping inter-community sentiment dependencies from social media time series.