De Clerck Bart, Rocha Luis E C, Van Utterbeeck Filip
Department of Economics, Ghent University, Ghent, Belgium.
Department of Mathematics, Royal Military Academy, Brussels, Belgium.
Appl Netw Sci. 2022;7(1):68. doi: 10.1007/s41109-022-00506-7. Epub 2022 Sep 28.
Recently proposed computational techniques allow the application of various maximum entropy network models at a larger scale. We focus on disinformation campaigns and apply different maximum entropy network models on the collection of datasets from the Twitter information operations report. For each dataset, we obtain additional Twitter data required to build an interaction network. We consider different interaction networks which we compare to an appropriate null model. The null model is used to identify statistically significant interactions. We validate our method and evaluate to what extent it is suited to identify communities of members of a disinformation campaign in a non-supervised way. We find that this method is suitable for larger social networks and allows to identify statistically significant interactions between users. Extracting the statistically significant interaction leads to the prevalence of users involved in a disinformation campaign being higher. We found that the use of different network models can provide different perceptions of the data and can lead to the identification of different meaningful patterns. We also test the robustness of the methods to illustrate the impact of missing data. Here we observe that sampling the correct data is of great importance to reconstruct an entire disinformation operation.
最近提出的计算技术允许在更大规模上应用各种最大熵网络模型。我们专注于虚假信息传播活动，并将不同的最大熵网络模型应用于从推特信息行动报告中收集的数据集。对于每个数据集，我们获取构建交互网络所需的额外推特数据。我们考虑不同的交互网络，并将其与适当的空模型进行比较。空模型用于识别具有统计显著性的交互。我们验证了我们的方法，并评估了它在多大程度上适合以无监督方式识别虚假信息传播活动成员的社区。我们发现这种方法适用于更大的社交网络，并能够识别用户之间具有统计显著性的交互。提取具有统计显著性的交互会导致参与虚假信息传播活动的用户比例更高。我们发现使用不同的网络模型可以提供对数据的不同认知，并能够识别不同的有意义模式。我们还测试了这些方法的稳健性，以说明缺失数据的影响。在此我们观察到，采样正确的数据对于重建整个虚假信息行动至关重要。
