| Title | A Graph Neural Network For Assessing The Affective Coherence Of Twitter Graphs |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Drakopoulos, Georgios, Giannoukou, Ioanna, Mylonas, Phivos, Sioutas, Spyros |
| Conference Name | 2020 IEEE International Conference on Big Data (Big Data) |
| Keywords | affective coherency, Big Data, Blogs, graph neural networks, high dimensional data, Linked data, neural network resiliency, pubcrawl, resilience, Resiliency, social graph resiliency, social networking (online), Task Analysis, Voting |
| Abstract | Graph neural networks (GNNs) is an emerging class of iterative connectionist models taking full advantage of the interaction patterns in an underlying domain. Depending on their configuration GNNs aggregate local state information to obtain robust estimates of global properties. Since graphs inherently represent high dimensional data, GNNs can effectively perform dimensionality reduction for certain aggregator selections. One such task is assigning sentiment polarity labels to the vertices of a large social network based on local ground truth state vectors containing structural, functional, and affective attributes. Emotions have been long identified as key factors in the overall social network resiliency and determining such labels robustly would be a major indicator of it. As a concrete example, the proposed methodology has been applied to two benchmark graphs obtained from political Twitter with topic sampling regarding the Greek 1821 Independence Revolution and the US 2020 Presidential Elections. Based on the results recommendations for researchers and practitioners are offered. |
| DOI | 10.1109/BigData50022.2020.9378492 |
| Citation Key | drakopoulos_graph_2020 |
A Graph Neural Network For Assessing The Affective Coherence Of Twitter Graphs