From anomaly detection to rumour detection using data streams of social platforms

Nguyên

et al. 2021

Information Sciences

Web platforms, especially social media, are facing a new and ever-evolving cyber threat operating at the information level. Their open nature allows a high velocity flow of rumours that emerge unexpectedly and spread quickly. While rumour detection has attracted many theoretical and practice studies, the timing of the detection is often neglected or not properly considered. Rumours often cause irreversible damage worldwide before being successfully detected. To address this, we approach early rumour detection from a streaming perspective. We present a just-in-time rumour detection framework that is built on top of the continuous scoring of rumour-related signals. To overcome the trade-off between timeliness and the coefficient of detection, our model treats social graphs as a data stream and computes the anomaly score of potential rumours at both the element-level and subgraph-level. This multi-level approach not only captures the propagation structure of rumours but also focuses on abnormal elements that are responsible for bootstrapping or amplifying the rumours (the 'explore vs exploit' effect). With extensive evaluations on our published benchmark, our model identifies rumours earlier than the baselines while achieving an even better detection coefficient.

Section: Rumour As Anomaly Signalsmentioning

confidence: 99%

Section: Anomaly Featuresmentioning

confidence: 99%

Section: Experimental Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

JUDO: Just-in-time rumour detection in streaming social platforms

Nguyên

et al. 2021

Information Sciences

2020 IEEE 36th International Conference on Data Engineering (ICDE)

“…Moreover, networks often comprise heterogeneous information including network structure and node features. While different types of information may be useful for network alignment, the lack of a common modality imposes challenges [37]. Information integration may be guided by path-based constraints and proximity rules [33].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Network Alignment with Unsupervised and Multi-order Convolutional Networks

Huynh

Vinh

Tâm

et al. 2020

Self Cite

Network alignment is the problem of pairing nodes between two graphs such that the paired nodes are structurally and semantically similar. A well-known application of network alignment is to identify which accounts in different social networks belong to the same person. Existing alignment techniques, however, lack scalability, cannot incorporate multi-dimensional information without training data, and are limited in the consistency constraints enforced by an alignment. In this paper, we propose a fully unsupervised network alignment framework based on a multi-order embedding model. The model learns the embeddings of each node using a graph convolutional neural representation, which we prove to satisfy consistency constraints. We further design a data augmentation method and a refinement mechanism to make the model adaptive to consistency violations and noise. Extensive experiments on real and synthetic datasets show that our model outperforms state-of-the-art alignment techniques. We also demonstrate the robustness of our model against adversarial conditions, such as structural noises, attribute noises, graph size imbalance, and hyper-parameter sensitivity.

Concurrency and Computation

Modified valence aware dictionary for sentiment reasoning classifier for detection and classification of Covid‐19 related rumors from social media data streams

Arora

Rani

Saxena

2022

The perpetual increase in social media data due to social distancing policy in practice and the low cost of communication has acquired an interest in rumor detection in social media. During the togetherness of the globalized world in the pandemic situation of Covid-19, social media applications drive excellent value to the analysts and journalists working in different domains. However, the unmoderated nature of social media users often leads to the spread of rumors due to a variety of opinions on government decisions, which becomes notoriously hard to detect. Also, the users may deny rumors as soon as they are debunked. This research extracts diffused information such as word cloud, hashtags, re-tweets and so forth and uses it as features. These features are applied in the sliding windows of data streams to detect tweets that may be rumors by validating using credible news sources. To address the challenge of veracity, tweets are classified into rumors and nonrumors. A modified unsupervised VADER sentiment classifier is proposed to further classify rumors tweets into amalgamated, unauthoritative, or exaggerated. It is revealed from the results that the proposed classifier is quite efficient in finding and classifying rumorous tweets that arise in critical situations.