Realtime analysis of information diffusion in social media

Taxidou, Io; Fischer, Peter M.

doi:10.14778/2536274.2536328

Cited by 25 publications

(13 citation statements)

References 26 publications

(40 reference statements)

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“…The notion of a social graph enables us to address rumour detection with techniques for data management. As such, the developed algorithms are also applicable to data of social platforms that can be transformed to a graph representation [54,45,23,42].…”

Section: A Model Of Social Platformsmentioning

confidence: 99%

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

et al. 2019

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Social platforms became a major source of rumours. While rumours can have severe real-world implications, their detection is notoriously hard: Content on social platforms is short and lacks semantics; it spreads quickly through a dynamically evolving network; and without considering the context of content, it may be impossible to arrive at a truthful interpretation. Traditional approaches to rumour detection, however, exploit solely a single content modality, e.g., social media posts, which limits their detection accuracy. In this paper, we cope with the aforementioned challenges by means of a multi-modal approach to rumour detection that identifies anomalies in both, the entities (e.g., users, posts, and hashtags) of a social platform and their relations. Based on local anomalies, we show how to detect rumours at the network level, following a graph-based scan approach. In addition, we propose incremental methods, which enable us to detect rumours using streaming data of social platforms. We illustrate the effectiveness and efficiency of our approach with a real-world dataset of 4M tweets with more than 1000 rumours.

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Section: A Model Of Social Platformsmentioning

confidence: 99%

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

et al. 2019

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“…The first step is collecting the dataset from the selected social media. It is important to note the problem related to real-time processing [15], [72]; social media owners are still placing restrictions on collecting real-time datasets. The second step is analyzing the collected dataset.…”

Section: ) Social Analysismentioning

confidence: 99%

Toward Information Diffusion Model for Viral Marketing in Business

AlSuwaidan¹,

Ykhlef²

2016

ijacsa

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Abstract-Current obstacles in the study of social media marketing include dealing with massive data and real-time updates have motivated to contribute solutions that can be adopted for viral marketing. Since information diffusion and social networks are the core of viral marketing, this article aims to investigate the constellation of diffusion methods for viral marketing. Studies on diffusion methods for viral marketing have applied different computational methods, but a systematic investigation of these methods has limited. Most of the literature have focused on achieving objectives such as influence maximization or community detection. Therefore, this article aims to conduct an in-depth review of works related to diffusion for viral marketing. Viral marketing has applied to business-to-consumer transactions but has seen limited adoption in business-to-business transactions. The literature review reveals a lack of new diffusion methods, especially in dynamic and large-scale networks. It also offers insights into applying various mining methods for viral marketing. It discusses some of the challenges, limitations, and future research directions of information diffusion for viral marketing. The article also introduces a viral marketing information diffusion model. The proposed model attempts to solve the dynamicity and large-scale data of social networks by adopting incremental clustering and a stochastic differential equation for business-to-business transactions.

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“…They also proposed an IP algorithm to estimate the influence and passivity of users based on their influence-passivity score. Taxidou [23] and Guille [24] analysed how information is diffused in social networks using the concept of information cascade. Another finding is that information cascade can help to identify the influential spreader by calculating the quantitative weight of individuals in the cascade.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring Social Relationships in Text Streams

Wang¹

2016

ICST Transactions on Scalable Information Systems

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Mining social relationships offers us an opportunity to gain insights from non-obvious relationships between individuals. Its applications can be seen in various scenarios ranging from market planning, fraud detection to the protection of national security. Most raw information related to social relationships are continuously generated by social networks in a form of text, for the reason that it has the lowest storage consumption while still possesses powerful expression abilities. However, when these continuous texts are aggregated together forming enormous text streams, applying existing data mining approaches will encounter efficiency or usability issues, either due to their overlook of the dynamic property of streams or the inapplicability of traditional store-then-process paradigm. In this paper, we specify the research gap and present a review report for dynamic text streams.

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Realtime analysis of information diffusion in social media

Cited by 25 publications

References 26 publications

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

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

Toward Information Diffusion Model for Viral Marketing in Business

Exploring Social Relationships in Text Streams

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