The PGNSC Benchmark: How Do We Predict Where Information Spreads?

Anonymous

Abstract: Social networks have become ideal vehicles for news dissemination because posted content is easily able to reach users beyond a news outlet's direct audience. Understanding how information is transmitted among communities of users is a critical step towards understanding the impact social networks have on real-world events. Two significant barriers in this vein of work are identifying user clusters and meaningfully characterizing these communities. Thus, we propose the PGNSC benchmark, which builds information pathways based on the audiences of influential news sources and uses their content to characterize the communities. We present methods of aggregating these news-source-centric communities and for constructing the community feature representations that are used sequentially to construct information pathway prediction pipelines.Lastly, we perform extensive experiments to demonstrate the performance of baseline pipeline constructions and to highlight the possibilities for future work.

Paper Type: long

Research Area: NLP Applications

Contribution Types: Publicly available software and/or pre-trained models, Data resources, Data analysis

Languages Studied: English