Given the rising prevalence of disinformation and fake news online, the detection of fake news in social media posts has become an essential task in the field of natural language processing (NLP). In this paper, we propose a fake detection model named, FakEDAMR that encodes textual content using the Abstract Meaning Representation (AMR) graph, a semantic representation of natural language that captures the underlying meaning of a sentence. The graphical representation of textual content holds longer relation dependency in very few distances. A new fake news dataset, FauxNSA, has been created using tweets from the Twitter platform related to ‘Nupur Sharma’ and ‘Agniveer’ political controversy. We represent each sentence of the tweet using AMR graph and then use this in combination with textual features to classify fake news. Experimental results on two different sets of features show that adding AMR graph features improves F1-score and accuracy. In the experiments, Random Forest with AMR-encoded features outperforms other models in Feature-set 1, achieving 88.90%, 89.48%, 87.09% accuracy and 85.92%, 88.69%, 86.70% F1-score on the FauxNSA, Covid19-FND, and KFN datasets, respectively. However, when Feature-set 2 is used, BiLSTM with AMR-encoded features emerges as the top-performing model. It achieves highest accuracy and F1-score of 93.96% and 91.96% on the dataset. It also maintains high performance on Covid19-FND and KFN datasets, with accuracy and F1-scores of 93.26% and 93.20% on Covid19-FND, and 93.52% and 93.52% on KFN, respectively.

Identifying and preserving community structures in a streaming graph is a very challenging task. However, many applications require the identification of these communities in very limited space and time. In this paper, we design Community Sketch, a small space data  structure that efficiently preserves communities. On query, it provides communities in constant time. With the use of community sketch data structure, a linear streaming community detection algorithm is proposed. Experimental results on the large real-world networks show that our algorithm outperforms other state-of-the-art algorithms in terms of quality metrics (NMI, F1-score, and WCC). Further, we propose an algorithm to produce benchmark network, namely, Temporal Community Benchmark Dataset (TCBD) which contains both true community labels and temporal information of edges. These synthetic networks are used to validate the proposed algorithm