Towards Multi-level Provenance Reconstruction of Information Diffusion on Social Media

Nies, Tom De; Taxidou, Io; Dimou, Anastasia; Verborgh, Ruben; Fischer, Peter M.; Mannens, Erik; Walle, Rik Van de

doi:10.1145/2806416.2806642

Cited by 15 publications

(20 citation statements)

References 14 publications

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“…The security or access restrictions or storage constraints of data base access and the concept of associating cost with trust has been described in [3]. The use of information cascades for tracing the path associated with a piece of news has been described in [6]. A news article has been described as having been influenced or transformed with edit activities which define the path of provenance to the final version of news as described in [7].…”

Section: Provenance Graph Computational Trust and Hierarchy Of Rolesmentioning

confidence: 99%

“…The use of information cascades has been used for tracing the path a piece of news has traversed in the social media graph initiating from the source and traversing the information influencers [6] in the relevant provenance path. Multiple sources can produce stories about the same event which may be grouped for summarization and mining [71] purposes as has been described in [72].…”

Section: Related Work In Story Topic Eventmentioning

confidence: 99%

“…A networked provenance system with migration of information between network nodes has been described in [3,4]. The use of concepts of provenance applied to news reports analysis has been described in [5][6][7][8][9]. The application of markov decision process to planning, event modelling has been described in [10,11].…”

Section: Introductionmentioning

confidence: 99%

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Learning topic description from clustering of trusted user roles and event models characterizing distributed provenance networks: a reinforcement learning approach

Mukherjee

Bandyopadhyay

2017

J Big Data

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This paper proposes a reinforcement learning based message transfer model for transferring news report messages through a selected path in a trusted provenance network with the objective of maximizing the reward values based on trust or importance based and network congestion or utility based cost measures. The reward values are calculated along a dynamically defined policy path connecting start topic or event node to a goal topic or event or issue nodes for incrementally defined time windows for a given network congestion situation. A hierarchy of agents of trusted roles is used to accomplish the sub-goals associated with sub-story or subtopic in the provenance structure where an agent role may assume the semantic role of the associated subtopic. The twitted news story thread or plan of events is defined in this work from the starting topic or event node to the goal topic or event node for incrementally defined intervals of time. The graphs are clustered into subtopic and these sub-goals or sub topic nodes of a topic node at every level of granularity are associated with cluster of news reports which describe activities associated with sub-goal or sub-topic events. Such cluster of nodes may also represent drilled down sequence of sub-events describing a sub-topic or sub-goal node. The policy path in a topic or story graph model is defined by applying reinforcement learning principles on dynamically defined event models associated with evolution of topic definition observed from incrementally acquired samples of input training data spanning multiple time windows. We provide a methodology for unifying similar provenance graph models for adapting and averaging the policy path classifiers associated with individual models to produce a reduced set of unified models derived during training. A minimum set cover of classifiers is identified for the models and a clustering procedure of the models is suggested based on these classifiers. Other database clustering methods have also been suggested as alternatives for clustering these models. A collection of unified models are identified from the models identified within a cluster and the policy path classifiers associated with these models provide the story or topic descriptions destined to goal topic or event nodes characterizing these models within a cluster.

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Section: Provenance Graph Computational Trust and Hierarchy Of Rolesmentioning

confidence: 99%

Section: Related Work In Story Topic Eventmentioning

confidence: 99%

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Learning topic description from clustering of trusted user roles and event models characterizing distributed provenance networks: a reinforcement learning approach

Mukherjee

Bandyopadhyay

2017

J Big Data

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“…This involves two different non-trivial tasks that are affected by rate limits – first obtaining all retweets for a popular tweet up to a certain point in time and next getting social graph information (follower-friend connections) among retweeters to chart out potential paths using which the information has spread across the Twitterverse. Although other researchers have continued to analyze diffusion through retweet graphs in fields such as politics, crisis response, and sports [12]–[15], similar results are not available for health topics.…”

Section: Introductionmentioning

confidence: 99%

On quantifying diffusion of health information on Twitter

Bakal

Kavuluru

2017

2017 IEEE EMBS International Conference on Biomedical &Amp; Health Informatics (BHI)

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With the increasing use of digital technologies, online social networks are emerging as major means of communication. Recently, social networks such as Facebook and Twitter are also being used by consumers, care providers (physicians, hospitals), and government agencies to share health related information. The asymmetric user network and the short message size have made Twitter particularly popular for propagating health related content on the Web. Besides tweeting on their own, users can choose to retweet particular tweets from other users (even if they do not follow them on Twitter.) Thus, a tweet can diffuse through the Twitter network via the follower-friend connections. In this paper, we report results of a pilot study we conducted to quantitatively assess how health related tweets diffuse in the directed follower-friend Twitter graph through the retweeting activity. Our effort includes (1). development of a retweet collection and Twitter retweet graph formation framework and (2). a preliminary analysis of retweet graphs and associated diffusion metrics for health tweets. Given the ambiguous nature (due to polysemy and sarcasm) of health relatedness of tweets collected with keyword based matches, our initial study is limited to ≈ 200 health related tweets (which were manually verified to be on health topics) each with at least 25 retweets. To our knowledge, this is first attempt to study health information diffusion on Twitter through retweet graph analysis.

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“…In many situations, however, we have to deal with legacy applications, for which we do not have the opportunity to modify the source code to introduce provenance-generating code, or we do not have the opportunity to recompile programs. Thus, in those cases, using application knowledge, provenance can alternatively be reconstructed retrospectively [23], [24]. Regardless of the approach, whether by instrumentation, logging, aspects, static methods, or reconstruction, at some point, a provenance record needs to be constructed.…”

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confidence: 99%

A Templating System to Generate Provenance

Moreau

Batlajery

Huynh

et al. 2018

IIEEE Trans. Software Eng.

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PROV-TEMPLATE is a declarative approach that enables designers and programmers to design and generate provenance compatible with the PROV standard of the World Wide Web Consortium. Designers specify the topology of the provenance to be generated by composing templates, which are provenance graphs containing variables, acting as placeholders for values. Programmers write programs that log values and package them up in sets of bindings, a data structure associating variables and values. An expansion algorithm generates instantiated provenance from templates and sets of bindings in any of the serialisation formats supported by PROV. A quantitative evaluation shows that sets of bindings have a size that is typically 40% of that of expanded provenance templates and that the expansion algorithm is suitably tractable, operating in fractions of milliseconds for the type of templates surveyed in the article. Furthermore, the approach shows four significant software engineering benefits: separation of responsibilities, provenance maintenance, potential runtime checks and static analysis, and provenance consumption. The article gathers quantitative data and qualitative benefits descriptions from four different applications making use of PROV-TEMPLATE. The system is implemented and released in the open-source library ProvToolbox for provenance processing. ! 1 INTRODUCTION P ROVENANCE has gained a lot of traction lately in various areas including the Web, legal notices 1 , climate science 2 , scientific workflows [1], [2], [3], computational reproducibility [4], emergency response [5], medical applications 3 , geospatial domain 4 , art and food. The recent standard PROV [6] of the World Wide Web Consortium defines provenance as "as a record that describes the people, institutions, entities, and activities involved in producing, influencing, or delivering a piece of data or a thing." In an increasing number of applications, provenance has become crucial in making systems accountable, by exposing how information flows in systems, and in helping users decide whether information is to be trusted. Provenance is not restricted to computer systems, it can also be used to describe how objects are transformed and people are involved in a physical system [5].Applications and use cases for provenance are well documented in the literature [7], [8], [9], [10]. They include making systems more auditable and accountable [11], reproducing results [12], deriving trust and classification [13], asserting attribution and generating acknowledgments [14], supporting predictive analytics [13], and facilitating traceability [15]. To enable such a powerful functionality, however, one needs to adapt or write applications, so that they generate provenance information, which can then be exploited to offer new benefits to their users.A number of approaches have been proposed to generate provenance: run-time, compile-time, and retrospectively. Runtime generation typically requires applications to be instrumented, and provenance generated accordingly [16],...

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Towards Multi-level Provenance Reconstruction of Information Diffusion on Social Media

Cited by 15 publications

References 14 publications

Learning topic description from clustering of trusted user roles and event models characterizing distributed provenance networks: a reinforcement learning approach

Learning topic description from clustering of trusted user roles and event models characterizing distributed provenance networks: a reinforcement learning approach

On quantifying diffusion of health information on Twitter

A Templating System to Generate Provenance

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