Birds of a feather check together: Leveraging homophily for sequential rumour detection

Lathiya, Shital; Dhobi, J. S.; Zubiaga, Arkaitz; Liakata, Maria; Procter, Rob

doi:10.1016/j.osnem.2020.100097

Cited by 6 publications

(4 citation statements)

References 52 publications

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“…To measure the performance of the proposed rumor detection model, following common practice [ 11 , 12 , 20 ], four methods were used, including Accuracy (Acc. ), Precision (P.), Recall (R.), and Fa - score (Fa) .…”

Section: Methodsmentioning

confidence: 99%

“…Five existing models were selected as benchmarks, including those proposed by Yang et al [ 39 ], Sun et al [ 30 ], Liang et al [ 16 ], Ma and Luo [ 23 ], and Lathiya et al [ 12 ]. Four machine learning methods were used to train the classifiers, including Naive Bayes (NB), Neural Network (NN), Support Vector Machine (SVM), and Logistic Regression (LR).…”

Section: Methodsmentioning

confidence: 99%

“…For Ma and Luo [ 23 ], we used the words contained in the Rumor Lexicon to replace their proposed bad words. With respect to Lathiya et al [ 12 ], ICTCLAS was used to build the part-of-speech tags of posts.…”

Section: Methodsmentioning

confidence: 99%

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A two-step rumor detection model based on the supernetwork theory about Weibo

et al. 2021

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Based on the supernetwork theory, a two-step rumor detection model was proposed. The first step was the classification of users on the basis of user-based features. In the second step, non-user-based features, including psychology-based features, content-based features, and parts of supernetwork-based features, were used to detect rumors posted by different types of users. Four machine learning methods, namely, Naive Bayes, Neural Network, Support Vector Machine, and Logistic Regression, were applied to train the classifier. Four real cases and several assessment metrics were employed to verify the effectiveness of the proposed model. Performance of the model regarding early rumor detection was also evaluated by separating the datasets according to the posting time of posts. Results showed that this model exhibited better performance in rumor detection compared to five benchmark models, mainly owing to the application of the supernetwork theory and the two-step mechanism.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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A two-step rumor detection model based on the supernetwork theory about Weibo

et al. 2021

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“…It is by no means limited to the often discussed case of political homophily [140]. For example, empirical social media studies identified weightbased homophily [141], journalistic homophily [142], homophily in rumor sharing [143], higher perceived homophily by users from collectivistic cultures [144], perceived homophily driving consumer purchase intentions [145] and credibility of information [146], homophilic effects in consumerwebsite relationships [147], homophily as factor for vlogger popularity [148], ideological hashtag homophily in marketing campaigns [149] and even homophily related to music preferences [150]. Apart from that, it is known in social psychology that "ingroups are seen as more variable than outgroups" [151] (especially in individualistic cultures).…”

Section: Near-term Guidelines For Risks Ia and Ib Rda •mentioning

confidence: 99%

Transdisciplinary AI Observatory—Retrospective Analyses and Future-Oriented Contradistinctions

2021

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In the last years, artificial intelligence (AI) safety gained international recognition in the light of heterogeneous safety-critical and ethical issues that risk overshadowing the broad beneficial impacts of AI. In this context, the implementation of AI observatory endeavors represents one key research direction. This paper motivates the need for an inherently transdisciplinary AI observatory approach integrating diverse retrospective and counterfactual views. We delineate aims and limitations while providing hands-on-advice utilizing concrete practical examples. Distinguishing between unintentionally and intentionally triggered AI risks with diverse socio-psycho-technological impacts, we exemplify a retrospective descriptive analysis followed by a retrospective counterfactual risk analysis. Building on these AI observatory tools, we present near-term transdisciplinary guidelines for AI safety. As further contribution, we discuss differentiated and tailored long-term directions through the lens of two disparate modern AI safety paradigms. For simplicity, we refer to these two different paradigms with the terms artificial stupidity (AS) and eternal creativity (EC) respectively. While both AS and EC acknowledge the need for a hybrid cognitive-affective approach to AI safety and overlap with regard to many short-term considerations, they differ fundamentally in the nature of multiple envisaged long-term solution patterns. By compiling relevant underlying contradistinctions, we aim to provide future-oriented incentives for constructive dialectics in practical and theoretical AI safety research.

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Graph Convolution-Based Joint Learning of Rumor with Content, User Credibility, Propagation Context, and Cognitive as Well as Emotion Signals

Nagaraj

Das

2021

Advances in Intelligent Systems and Computing

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Birds of a feather check together: Leveraging homophily for sequential rumour detection

Cited by 6 publications

References 52 publications

A two-step rumor detection model based on the supernetwork theory about Weibo

A two-step rumor detection model based on the supernetwork theory about Weibo

Transdisciplinary AI Observatory—Retrospective Analyses and Future-Oriented Contradistinctions

Graph Convolution-Based Joint Learning of Rumor with Content, User Credibility, Propagation Context, and Cognitive as Well as Emotion Signals

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