Detection of Rumors and Their Sources in Social Networks: A Comprehensive Survey

Jaeyoung Choi; Otabek Sattarov

REVIEW 4 major objections 5 minor 159 references

Detection of Rumors and Their Sources in Social Networks: A Comprehensive Survey

T0 review · 4 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read This survey claims to be the first to treat rumor detection and rumor-source detection as one formal problem family, defining classification, inference, and joint detection.

desk verdict Useful survey of two subfields and the joint idea, but the 'first joint survey' claim is unproven and the joint section has concrete bugs ([128]=[135], Algorithm 3). read the letter →

arxiv 2501.05292 v1 pith:CBA6LMGL submitted 2025-01-09 cs.SI

Otabek Sattarov , Jaeyoung Choi This is my paper · ORCID

classification cs.SI

keywords rumordetectionsourcejointinferencesocialnetworksmisinformationlocalizationgraphneuralsurvey

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to show that the question “is this information true?” and the question “who first spread it?” belong to a single problem family. Most prior surveys cover one or the other; this one organizes both plus their joint treatment, and gives each a formal definition. It matters because a rumor's credibility and its origin are connected in practice: reliable sources tend to spread truth, and true news tends to be reported by many independent people. If the joint framing holds, future systems could localize a rumor and assess its veracity from the same cascade data.

What carries the argument

The load-bearing object is the joint detection function from Definition 5, written as $f: (T, G_I) \to (\{0,1\}, \hat{S})$, which wraps the rumor-detection classifier and the rumor-source estimator into a single simultaneous inference target. That function is supported by three smaller machinery pieces: the network structure and propagation model (SI, SIR, SIRS, or independent cascade) that generate the infection graph, the snapshot type that determines what data the estimator sees, and the estimator itself, either graph-based centrality such as the Jordan center or a probability-based rule such as the maximum-likelihood estimator. The survey's entire organization hangs on whether this joint function is a meaningful target rather than two tasks that happen to share data.

What would settle it

A bibliographic search turning up a peer-reviewed survey published before 2025 that already treats rumor detection, rumor-source detection, and their joint consideration with formal definitions would falsify the paper's claim to be the first. Empirically, a study showing that knowing the set of estimated sources adds no predictive power for veracity, or that source multiplicity is unrelated to truthfulness, would undercut the joint problem's motivation.

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Extended reading notes

Core claim

On its own terms, the paper's discovery is taxonomic: the literature on misinformation in social networks separates into rumor detection (a classification problem $f: T \to \{0,1\}$), rumor-source detection (an inference problem $f: G_I \to \hat{S}$), and joint detection $f: (T, G_I) \to (\{0,1\}, \hat{S})$, with the last almost unstudied. The paper assembles the existing algorithms under this scheme, classifies source-detection work by the number of sources and by snapshot type (complete, partial, or sensor-monitored), and identifies exactly two existing joint approaches, one driven by source reliability and one by the number of independent reporters. It also covers the opposite problem of hiding rumors and their sources, including protocols that keep the source at the leaf of the infection graph. The intended contribution is a map that lets a researcher see what has been solved and where the joint problem remains open.

Load-bearing premise

The survey assumes that rumor detection and source detection are genuinely coupled, through source reliability and the expected number of independently reporting sources, so that solving them jointly is a meaningful goal rather than two independent tasks.

Editorial extensions

If this is right

Researchers can place any new rumor or source method into the survey's taxonomy: content-, propagation-, source-, or hybrid-based for rumor detection, and single- versus multiple-source with complete, partial, or sensor snapshots for source detection.
The two existing joint algorithms, one based on source reliability and one based on the number of sources, demonstrate that joint inference is technically possible and suggest it can outperform running the two tasks separately.
The adaptive-diffusion line of work implies that source detection methods must be evaluated against adversaries that actively obfuscate the infection graph, not just against benign cascades.
The challenges table points to concrete next steps: multimodal data integration, temporal alignment between rumor evolution and network dynamics, user-behavior modeling under adversarial manipulation, and scalable and interpretable joint algorithms.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A direct empirical test of the joint framing would benchmark the two existing joint approaches against their sequential counterparts on the same cascade data, measuring whether coupling actually raises detection probability or F1; the survey itself stops at cataloging rather than comparing.
Definition 5 suggests a modular architecture that the current joint papers only partially exploit: a source estimator can output a reliability-weighted candidate set, and a rumor classifier can consume that set as an additional feature, so the two tasks reinforce each other iteratively.
The source-hiding section implies that any rumor detector relying on source credibility is vulnerable to adversarial propagation timing, so robust detectors may need to treat estimated sources as adversarial inputs rather than trusted ground truth.
The number-of-sources argument would be testable on real event datasets: if a rumor is identified by a small number of independent reporters and true news by many, then a system could classify veracity from source multiplicity alone, a hypothesis the survey does not evaluate empirically.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

You should know: this is a usable survey, but its central novelty claim is shaky and the joint-detection section has concrete errors. The paper maps rumor detection and rumor-source detection side by side, introduces formal definitions for both plus a joint problem, and organizes source-detection work by snapshot type and number of sources. That taxonomy is genuinely helpful. The section on hiding rumor sources is a nice addition, since most surveys stop at detection. The tables summarizing algorithms, evaluation metrics, and challenges are also a decent starting point for someone entering the area.

The soft spots are real but mostly fixable. First, the claim to be the first survey covering rumor detection, source detection, and their joint consideration is load-bearing but never demonstrated. There is no systematic comparison with earlier surveys; the authors just assert it. That needs to be substantiated or softened. Second, the stress-test note holds up: reference [128] and reference [135] are the same paper by Seo, Mohapatra, and Abdelzaher. It appears as a joint algorithm in Section III-D and also as a single-source sensor-monitoring estimator in Tables V and VI. That double-counting guts the joint-detection category down to one paper, SourceCR [134], unless the authors explain why the duplicate is genuinely joint. Third, Algorithm 3 does not implement Definition 5. It runs source detection and then rumor detection sequentially, not a joint function of (T, G_I), and line 11 repeats the rumor condition instead of the non-rumor branch, making the else-if unreachable. That is a concrete bug in a paper that advertises a formal joint problem. Fourth, the web spam papers [69], [70], [72] sit under rumor detection with no evident connection to rumors; that looks like careless categorization.

On the positive side, the formal definitions are clean restatements of existing concepts, not new theory, and the paper is honest that the joint problem is a combination. No result is fabricated, and the citation pattern is not self-serving. The defects are concentrated in the joint section and the novelty argument, not in the core survey material.

Who is this for? A graduate student or researcher wanting a structured overview of rumor source detection and the joint problem could get value from the taxonomy and tables, but they should verify the entries. It deserves a serious referee because the errors are concrete and correctable, and the survey fills a real bibliographic gap if Duplicate reference and Algorithm 3 are fixed. I would send it to peer review with major revision, and I would not cite it in its current form.

Referee Report

4 major / 5 minor

Summary. This manuscript is a survey of rumor detection and rumor-source detection on social networks, organized around three problems: rumor detection, rumor-source detection, and their joint consideration. It offers formal definitions (Definitions 2, 4, and 5), taxonomies of algorithms, comparative summary tables, a discussion of hiding and obfuscating rumors and sources, and a list of research challenges and future directions. The paper claims to be the first survey covering all three problems together, with the joint problem formalized as a simultaneous classification-and-estimation task.

Significance. If the coverage and taxonomy are made reliable, the survey would be a useful entry point for researchers: the three-problem framing, the formal problem definitions, the snapshot classification (complete, partial, sensor-monitoring), and the inclusion of source-obfuscation literature are valuable organizational devices. The paper also credits prior work by explicitly presenting the joint problem as a combination of the two individual problems, rather than as a newly derived formalism, and it provides several structured summary tables that help the reader navigate the area. However, the central novelty claim and the integrity of the joint-detection taxonomy are not yet established, so the contribution as a 'first comprehensive survey' is currently conditional.

major comments (4)

[Section I, Contribution (i)] The claim that this is the first survey to cover rumor detection, rumor-source detection, and their joint consideration is load-bearing, but the manuscript does not provide the comparison needed to establish it. It reports no systematic search, no inclusion/exclusion criteria, and no analysis of the earlier surveys cited in the paper ([3], [7], [8], [9], [12], [25], [26], [99], [144]) against the three-problem taxonomy. Please add such a comparison or explicitly qualify the novelty claim.
[Section III-D and Tables V-VI] Reference [128] and reference [135] are the same paper (Seo, Mohapatra, and Abdelzaher, SPIE Defense, Security, and Sensing, 2012). The survey uses it as a joint-detection algorithm in Section III-D and also as a single-source sensor-monitoring estimator in Section III-C and Tables V and VI. This double-counting undermines the joint-detection taxonomy: if the paper is genuinely joint, it should not be listed only as a single-source estimator; if it is not joint, the joint-algorithm section contains only [134]. Please deduplicate and correct the taxonomy accordingly.
[Algorithm 3] Algorithm 3 does not implement Definition 5. Definition 5 defines a single function f: (T, G_I) -> ({0,1}, hat{S}) that returns both outputs simultaneously, whereas the algorithm first estimates sources from the infection graph and then runs a separate rumor-detection function on T. In addition, line 11 repeats the condition 'T is regarded as a rumor' from line 9, making the 'else if' branch unreachable and the non-rumor output (f(T)=1) impossible. Please correct the condition and either present the algorithm as a sequential pipeline or describe a genuinely joint inference loop.
[Section III-A4 and Tables II-III] The 'hybrid and other machine learning-based approaches' category includes web-spam papers [69], [70], and [72]. The surrounding text itself describes these as web-spam detection, not rumor detection, and no explicit bridge to rumors is provided. Including them as rumor-detection methods misrepresents the surveyed literature; either remove them or explain the intended connection (e.g., as feature/algorithmic precursors).

minor comments (5)

[Table VII] The row label 'Ruomor center' should read 'Rumor center'.
[Section II-C] The sentence 'It is known that [135] rumors are usually initiated by a small number of people' has an awkward citation placement; rephrase to 'It is known [135] that rumors are usually initiated...'.
[Section IV-A] References [137] and [138] describe steganographic file systems in social networks; the relevance to 'hiding rumors' specifically should be stated explicitly, since the current text describes hiding arbitrary information.
[Table IV] The entry 'Deep-Faked' should be 'DEAP-FAKED' to match the cited method and the text in Section III-A1.
[Algorithm 1] Algorithm 1 applies f before describing what f does; consider a wording such as 'Run a rumor-detection algorithm f on T' to avoid giving the impression that f is assigned only afterward.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: this survey contains no fitted-parameter predictions or self-citation-derived derivations; its formal definitions and quoted external equations are self-contained descriptive content.

full rationale

This is a survey paper, not a derivation-driven work, and no claimed result is obtained by fitting, by renaming, or by importing an unverified self-citation as proof. The central formal statements, Definitions 2, 4, and 5, are explicit problem formalizations with no estimation step; Definition 5 is explicitly presented as the simultaneous combination of the two prior problems, not as a derived consequence. The only quantitative-looking formula, Eq. (11), is quoted verbatim from Fanti et al. [139] and is attributed to that external source rather than derived here, so it is not circular. The novelty claim that this is the first survey covering rumor detection, rumor-source detection, and their joint consideration is an unverified empirical assertion about the literature, but an unsupported novelty claim is a correctness or completeness risk, not a circularity: the claim does not make the survey's taxonomy equivalent to its inputs, and no parameter is fitted to any subset of the surveyed results and then renamed as a prediction. The self-citations such as [100]-[102] and [152] are ordinary references to the authors' earlier source-detection papers and are not load-bearing for any uniqueness argument, theorem, or fitted estimate. The internal issues noted by a skeptical reader, such as reference [128] and [135] potentially denoting the same Seo, Mohapatra, and Abdelzaher paper, and Algorithm 3 describing a sequential procedure rather than a literal implementation of Definition 5's simultaneous function, are organizational or correctness defects in a survey; they do not exhibit a reduction of any output to its input by construction. Accordingly, no circular step can be quoted from the manuscript, and the appropriate score is 0.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The paper contributes no free parameters and no new entities. It depends on standard propagation models taken from prior work and on the survey-specific premise that joint detection is a coherent problem.

assumptions (2)

domain assumption Rumor propagation on social networks is modeled by epidemic-style state models such as SI, SIR, SIS, SIRS, and the independent cascade model.
Introduces the propagation models in Section II-B-2 from the literature; the survey's source-detection taxonomy is built on them.
ad hoc to paper Rumor detection and rumor source detection are coupled through source reliability and the number of sources, making a joint problem meaningful.
Section II-C presents these two links as the reasons for the joint problem; this premise drives the survey's claimed contribution.

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Cite this review

Pith. "Pith review of Detection of Rumors and Their Sources in Social Networks: A Comprehensive Survey." pith.science (2026). https://pith.science/paper/CBA6LMGL

@misc{pith2026250105292,
  author       = {Pith},
  title        = {Pith review of: Detection of Rumors and Their Sources in Social Networks: A Comprehensive Survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CBA6LMGL}},
  note         = {Machine review of arXiv:2501.05292}
}

abstract

With the recent advancements in social network platform technology, an overwhelming amount of information is spreading rapidly. In this situation, it can become increasingly difficult to discern what information is false or true. If false information proliferates significantly, it can lead to undesirable outcomes. Hence, when we receive some information, we can pose the following two questions: $(i)$ Is the information true? $(ii)$ If not, who initially spread that information? % The first problem is the rumor detection issue, while the second is the rumor source detection problem. A rumor-detection problem involves identifying and mitigating false or misleading information spread via various communication channels, particularly online platforms and social media. Rumors can range from harmless ones to deliberately misleading content aimed at deceiving or manipulating audiences. Detecting misinformation is crucial for maintaining the integrity of information ecosystems and preventing harmful effects such as the spread of false beliefs, polarization, and even societal harm. Therefore, it is very important to quickly distinguish such misinformation while simultaneously finding its source to block it from spreading on the network. However, most of the existing surveys have analyzed these two issues separately. In this work, we first survey the existing research on the rumor-detection and rumor source detection problems with joint detection approaches, simultaneously. % This survey deals with these two issues together so that their relationship can be observed and it provides how the two problems are similar and different. The limitations arising from the rumor detection, rumor source detection, and their combination problems are also explained, and some challenges to be addressed in future works are presented.

Figures

Figures reproduced from arXiv: 2501.05292 by the authors.

**Figure 2.** Example of propagation snapshot. A complete snapsho [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Examples of Estimators. The estimator on the left is t [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: A hide-and-seek game of rumor sources [142]. In the ga [PITH_FULL_IMAGE:figures/full_fig_p016_4.png]

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Reference graph

Works this paper leans on

159 extracted references · 75 canonical work pages

[128]

Altarelli, A

F. Altarelli, A. Braunstein, L. Dall’Asta, A. Lage-Ca stellanos, and R. Zecchina, Bayesian inference of epidemics on networks vi a belief propagation. Phys. Rev. Lett vol. 112, no. 11, 2014, Art. no. 118701

work page 2014
[135]

IEEE Confer- ence on Computer Communications 2020

Shan Qu, Ziqi Zhao, Luoyi Fu, Xinbing Wang, Jun Xu Joint Inference on Truth/Rumor and Their Sources in Social Networks. IEEE Confer- ence on Computer Communications 2020

work page 2020
[134]

Expert Systems With Applications February 16, 2024

Ravi Kishore Devarapalli, Anupam Biswas Estimating r umor source in social networks using incomplete observer information. Expert Systems With Applications February 16, 2024

work page 2024
[69]

Dennis Fetterly, Mark Manasse, and Marc Najork Detecti ng phrase-level duplication on the World Wide Web In Proceedings of the 28th Annual International ACM SIGIR Conference , New Y ork, NY , 170–177.2005

2005
[70]

Alexandros Ntoulas, Marc Najork, Mark Manasse, and Den nis Fetterly Detecting spam web pages through content analysis In Proceedings of the 15th International Conference on W orld Wide W eb , ACM, New Y ork, NY , 83–92
[72]

In Machine Learning, 2 (2010), 207–225

Jacob Abernethy, Olivier Chapelle, and Carlos Castill o Graph regular- ization methods for web spam detection. In Machine Learning, 2 (2010), 207–225

2010
[3]

In arXiv:1807.03505

Juan Cao, Junbo Guo, Xirong Li, Zhiwei Jin, Han Guo, Jinta o Li Auto- matic Rumor Detection on Microblogs: A Survey. In arXiv:1807.03505

arXiv
[7]

Alzanin a, Aqil M

Samah M. Alzanin a, Aqil M. Azmi Detecting rumors in socia l media: A survey. In Procedia Computer Science , V olume 142, 2018, Pages 294-300

2018
[8]

Rumor Detection on Social Media: Datasets, Methods and Opportunities

Quanzhi Li, Qiong Zhang, Luo Si, Yingchi Liu Rumor Detec- tion on Social Media: Datasets, Methods and Opportunities. In arxiv/papers/1911/1911.07199 ,

work page Pith review arXiv 1911
[9]

Wen, Shui Y u, Y

Jiaojiao Jiang, S. Wen, Shui Y u, Y . Xiang, Wanlei Zhou Ide ntifying Propagation Sources in Networks: State-of-the-Art and Com parative Studies. In IEEE Communications Surveys and Tutorials , VOL. 19, NO. 1, FIRST QUARTER 2017

2017
[12]

Sharma Rumors detec tion, veriﬁcation and controlling mechanisms in online social ne tworks: A survey

Mohammad Ahsan, Madhu Kumari, T.P . Sharma Rumors detec tion, veriﬁcation and controlling mechanisms in online social ne tworks: A survey. In Online Social Networks and Media , V olume 14, November 2019, 100050

2019
[25]

Zhou and R

X. Zhou and R. Zafarani A Survey of Fake News: Fundamenta l Theories, Detection Methods, and Opportunities. In ACM Computing Surveys , V olume 53 Issue 5 Article No.: 109 pp 1–40
[26]

In Applied sciences , 2023, 13, 11877

Y anping Shen, Qingjie Liu, Na Guo , Jing Y uan and Y anqing Y ang Fake News Detection on Social Networks: A Survey. In Applied sciences , 2023, 13, 11877

2023
[99]

Rooting Out the Rumor Culp rit from Suspects

Dong, W., Zhang, W., Tan, C.W. Rooting Out the Rumor Culp rit from Suspects. In Proceedings of the IEEE International Symposi um on Information Theory (ISIT), Istanbul, Turkey, 7–12 July 201 3

work page
[144]

Bazzaz Abkenar, M

S. Bazzaz Abkenar, M. Haghi Kashani, E. Mahdipour, S. M ahdi Jameii Big data analytics meets social media: A systematic r eview of techniques, open issues, and future directions. Telematics and Informatics, V olume 57, March 2021, 101517

work page 2021

Show all 159 references

[1]

Eugene Stanley, and Wal ter Quat- trociocchi The spreading of misinformation online

Michela Del Vicario, Alessandro Bessi, Fabiana Zollo, F abio Petroni, Antonio Scala, Guido Caldarelli, H. Eugene Stanley, and Wal ter Quat- trociocchi The spreading of misinformation online. In PNAS, January 4, 2016 113 (3) 554-559

2016
[2]

R, Teaching writing in the (new) era of fake new s

Skinnell. R, Teaching writing in the (new) era of fake new s. In Coll Compos Commun , 72(4):546–569
[4]

In Journal of Management Science and Engineering , V olume 7, Issue 1, March 2022, Pages 36-47

Xuejun Ding, Xiaxia Zhang, Ruoshi Fan, Qiaochu Xu, Kyle H unt, Jun Zhuang, Rumor recognition behavior of social media user s in emergencies. In Journal of Management Science and Engineering , V olume 7, Issue 1, March 2022, Pages 36-47

2022
[5]

In Personal and Ubiquitous Computing , V olume 27, pages 1469–1482, (2023)

Bo Y ang, Rong Zhang, Xusen Cheng and Chuang Zhao Explorin g infor- mation dissemination effect on social media: an empirical i nvestigation. In Personal and Ubiquitous Computing , V olume 27, pages 1469–1482, (2023)

2023
[6]

V osoughi, D

S. V osoughi, D. Roy, and S. Aral The spread of true and fals e news online. In Science , VOL. 359, NO. 6380
[10]

Wen, Shui Y u, Y

Jiaojiao Jiang, S. Wen, Shui Y u, Y . Xiang, Wanlei Zhou Ru mor Source Identiﬁcation in Social Networks with Time-V arying Topolo gy In IEEE Transactions on Dependable and Secure Computing , Feb. 2018, pp. 166-179, vol. 15

2018
[11]

2023 pp 172–188

Weimin Li, Chang Guo, Y anxia Liu, Xiaokang Zhou, Qun Jin , Mingjun Xin Rumor source localization in social networks based on in fection potential energy In Information Sciences , V olume 634 Issue C Jul. 2023 pp 172–188

2023
[13]

In Discrete Dynamics in Nature and Society , V olume 2020 Article ID 6724815

Rachid Ghazzali,1Amine El Bhih, Adil El Alami Laarouss i,and Mostafa Rachik Modeling a Rumor Propagation in Online Social Networ k: An Optimal Control Approach. In Discrete Dynamics in Nature and Society , V olume 2020 Article ID 6724815

2020
[14]

In Social Network Analysis and Mining , V olume 13, article number 91, (2023)

Damian Fraszczak Detecting rumor outbreaks in online s ocial networks. In Social Network Analysis and Mining , V olume 13, article number 91, (2023)

2023
[15]

In IEEE 13th International Conference on Data Mining , IEEE,2013.1103-1108

Kwon S, Cha M, Jung K, Prominent features of rumor propag ation in online social media. In IEEE 13th International Conference on Data Mining , IEEE,2013.1103-1108

2013
[16]

Z. Qian, S. Tang, X. Zhang, Z. Zheng The independent spre aders involved SIR rumor model in complex networks. In Phys. A: Stat. Mech. Appl. , 429 (2015) 95102,

2015
[17]

In Physics Letters A , V olume 368, Issue 6, 3 September 2007, Pages 458-463

Jie Zhou a, Zonghua Liu a b, Baowen Li Inﬂuence of network structure on rumor propagation. In Physics Letters A , V olume 368, Issue 6, 3 September 2007, Pages 458-463

2007
[18]

Characterizing t he interactions between classical and community-aware centrality measure s in complex networks

Rajeh, S., Savonnet, M., Leclercq, E. Characterizing t he interactions between classical and community-aware centrality measure s in complex networks. In Sci Rep , 11, 10088 (2021)

2021
[19]

On community str ucture in complex networks: challenges and opportunities

Cheriﬁ, H., Palla, G., Szymanski, B.K. On community str ucture in complex networks: challenges and opportunities. In Appl Netw Sci , 4, 117 (2019)

2019
[20]

MayankM M, Sharma. S. Sharma. R DEAP-FAKED: Knowledge g raph- based approach for fake news detection. In In Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Ne tworks Analysis and Mining (ASONAM) , November 2022; pp. 47–51

2022
[21]

L, Y ang

Hu. L, Y ang. T, Zhang. L, Zhong. W, Tang. D, Shi. C, Duan. N , Zhou. M Compare to the knowledge: Graph neural fake news detection with external knowledge. In 11th International Joint Conference on Natural Language Processing, Online, 1–6 August 2021; pp. 754–763

2021
[22]

J.Z, Pavlova

Pan. J.Z, Pavlova. S, Li. C, Li. N, Li. Y , Liu. J, Content- based fake news detection using knowledge graphs. In In Proceedings of the International Semantic W eb Conference, October 2018; pp. 669–683

2018
[23]

J.A, Khan

Nasir. J.A, Khan. O.S, V arlamis. I, Fake news detection : A hybrid CNN- RNN based deep learning approach. In Int. J. Inf. Manag. Data Insights , 2021, 1, 100007. 22

2021
[24]

Zhou. X, Jain. A, Phoha. V .V , Zafarani. R, Fake news earl y detection: A theory-driven model. In Digit. Threat. Res. Pract , 2020, 1, 1–25

2020
[27]

Ganesh, L

A. Ganesh, L. Massoulie, D. Towsley The effect of networ k topology on the spread of epidemics. Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societi es 13-17 March 2005

2005
[28]

A, Arora

Choudhary. A, Arora. A, Linguistic feature-based lear ning model for fake news detection and classiﬁcation. In Expert Syst. Appl. , 2020, 169, 114171

2020
[29]

P .K, Agrawal

V erma. P .K, Agrawal. P , Amorim. I, Prodan. R, WELFake: W ord Embedding Over Linguistic Features for Fake News Detection . In IEEE Trans. Comput. Soc. Syst. , 2021, 8, 881–893

2021
[30]

M, Imtiaz

Umer. M, Imtiaz. Z, Ullah. S, Mehmood. A, Choi. G.S, On. B .-W, Fake News Stance Detection Using Deep Learning Architectur e (CNN- LSTM). In IEEE Access , 2020, 8, 156695–156706

2020
[31]

Cao. J, Qi. P , Sheng. Q, Y ang. T, Guo. J, Li. J, Exploring t he role of visual content in fake news detection. In Disinformation, Misinformation, and Fake News in Social Media: Emerging Res earch Challenges and Opportunities , Springer: Cham, Switzerland, 2020; pp. 141–161

2020
[32]

Qi. P , Cao. J, Y ang. T, Guo. J, Li. J, Exploiting multi-do main visual information for fake news detection. In Proceedings of the 2019 IEEE international conference on data mining (ICDM) , November 2019; pp. 518–527

2019
[33]

Carlos Castillo, Marcelo Mendoza, and Barbara Poblete Information credibility on Twitter In Proceedings of the 20th International Confer- ence on W orld Wide W eb, 2011

2011
[34]

Song. C, Ning. N, Zhang. Y , Wu. B, A multimodal fake news detection model based on crossmodal attention residual and multichannel convolutional neural networks. In Inf. Process. Manag. , 2020, 58, 102437

2020
[35]

In Journal of Management Information Systems , 2020, 58, 102437

Dongsong Zhang, Lina Zhou, Juan Luo Kehoe, and Isil Y aku t Kilic What Online Reviewer Behaviors Really Matter? Effects of ve rbal and nonverbal behaviors on detection of fake online reviews. In Journal of Management Information Systems , 2020, 58, 102437

2020
[36]

In Proceedings of the 9th International AAAI Conference on W eb and Social Medi a, 2015

Tanushree Mitra and Eric Gilbert CREDBANK: A large-sca le social media corpus with associated credibility annotations. In Proceedings of the 9th International AAAI Conference on W eb and Social Medi a, 2015

2015
[37]

V .K, Ghosh

Singh. V .K, Ghosh. I, Sonagara. D, Detecting fake news s tories via multimodal analysis. In J. Assoc. Inf. Sci. Technol. , 2021, 72, 3–17

2021
[38]

Khattar. D, Goud. J.S, Gupta. M, V arma. V , Mvae: Multimo dal variational autoencoder for fake news detection. In In The W orld Wide W eb Conference., USA, 2019; pp. 2915–2921

2019
[39]

I, Alonso-Bartolome

Segura-Bedmar. I, Alonso-Bartolome. S, Multimodal Fa ke News Detec- tion. In Information., 2022, 13, 284

2022
[40]

Wang. H, Wang. S, Han. Y , Detecting fake news on Chinese s ocial media based on hybrid feature fusion method. In Expert Syst. Appl. , 2022, 208, 118111

2022
[41]

F, Frasca

Monti. F, Frasca. F, Eynard. D, Mannion. D, Bronstein. M .M, Fake news detection on social media using geometric deep learnin g. In arXiv 2019, arXiv:1902.06673, 2019

2019 arXiv
[42]

Silva. A, Han. Y , Luo. L, Karunasekera. S, Leckie. C, Pro pagation2V ec: Embedding partial propagation networks for explainable fa ke news early detection. In Inf. Process. Manag. , 2021, 58, 102618

2021
[43]

G, Siciliano

Barnab` o. G, Siciliano. F, Castillo. C, Leonardi. S, Na kov. P , Martino. G.D.S, Silvestri. F, Deep active learning for misinformati on detection using geometric deep learning. In Online Soc. Netw. Media , 2023, 33, 100244

2023
[44]

Song. C, Teng. Y , Zhu. Y , Wei. S, Wu. B, Dynamic graph neur al network for fake news detection. In Neurocomputing, 2022, 505, 362–374

2022
[45]

Jeong. U, Ding. K, Cheng. L, Guo. R, Shu. K, Liu. H, Nothin g stands alone: Relational fake news detection with hypergraph neur al networks. In In Proceedings of the 2022 IEEE International Conference on Big Data (Big Data) , Japan, 17–20 December 2022; pp. 596–605

2022
[46]

Y , Karunasekera

Han. Y , Karunasekera. S, Leckie. C, Graph neural networ ks with continual learning for fake news detection from social medi a. In arXiv 2020, arXiv:2007.03316

2020 arXiv
[47]

Wei. L, Hu. D, Lai. Y , Zhou. W, Hu. S, A Uniﬁed Propagation Forest- based Framework for Fake News Detection. In In Proceedings of the 29th International Conference on Computational Linguisti cs,, October 2022; pp. 2769–2779

2022
[48]

T, Wakamiya

Murayama. T, Wakamiya. S, Aramaki. E, Kobayashi. R, Mod eling the spread of fake news on Twitter. In PLoS ONE , 2021, 16, e0250419

2021
[49]

Ni. S, Li. J, Kao. H.-Y , MV AN: Multi-View Attention Netw orks for Fake News Detection on Social Media. In IEEE Access , 2021, 9, 106907–106917

2021
[50]

Science, 59, 6380 (2018), 1146–1151

Soroush V osoughi, Deb Roy, and Sinan Aral The spread of t rue and false news online. Science, 59, 6380 (2018), 1146–1151

2018
[51]

K, Mahudeswaran

Shu. K, Mahudeswaran. D, Wang. S, Liu. H, Hierarchical p ropagation networks for fake news detection: Investigation and exploi tation. In In Proceedings of the International AAAI Conference onW eb and Social Media, June 2020; V olume 14, pp. 626–637

2020
[52]

M, Moosavi

Davoudi. M, Moosavi. M.R, Sadreddini. M.H, DSS: A hybri d deep model for fake news detection using propagation tree and sta nce network. In Expert Syst. Appl. , 2022, 198, 116635

2022
[53]

Shu. K, Wang. S, Liu. H, Beyond news contents: The role of social context for fake news detection. In In Proceedings of the twelfth ACM International Conference on W eb Search and Data Mining , February 2019; pp. 312–320

2019
[54]

V .H, Sugiyama

Nguyen. V .H, Sugiyama. K, Nakov. P , Kan. M.-Y , Leveragi ng social context for fake news detection using graph representation . In In Proceedings of the 29th ACM International Conference on Inf ormation & Knowledge Management , November 2020; pp. 1165–1174

2020
[55]

N, Mohan

Sitaula. N, Mohan. C.K, Grygiel. J, Zhou. X, Credibilit y-based fake news detection. In In Disinformation, Misinformation, and Fake News in Social Media: Emerging Research Challenges and Opportun ities, Switzerland, 2020; pp. 163–182

2020
[56]

Y uan. C, Ma. Q, Zhou. W, Han. J, Hu. S, Early Detection of F ake News by Utilizing the Credibility of News, Publishers, and U sers based on Weakly Supervised Learning. In In Proceedings of the 28th International Conference on Computational Linguisti cs, December 2020; pp. 5444–5454

2020
[57]

Luvembe. A.M, Li. W, Li. S, Liu. F, Xu. G, Dual emotion bas ed fake news detection: A deep attention-weight update approa ch. In Inf. Process. Manag., 2023, 60, 103354

2023
[58]

D, Chinnaiah

Ramalingam. D, Chinnaiah. V , Fake proﬁle detection tec hniques in large-scale online social networks: A comprehensive revie w. In Comput. Electr . Eng., 2018, 65, 165–177

2018
[59]

S, Ferrara

Kudugunta. S, Ferrara. E, Deep neural networks for bot d etection. In Inf. Sci. , 2018, 467, 312–322

2018
[60]

R.R, Karbasi

Rostami. R.R, Karbasi. S, Detecting Fake Accounts on Tw itter Social Network Using Multi-Objective Hybrid Feature Selection Ap proach. In W ebology, 2020, 17, 1–18

2020
[61]

T, Y ang

Gao. T, Y ang. J, Peng. W, Jiang. L, Sun. Y , Li. F, A Content - Based Method for Sybil Detection in Online Social Networks v ia Deep Learning. In IEEE Access , 2020, 8, 38753–38766

2020
[62]

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery , 5 (2017), e1209

Marco Viviani and Gabriella Pasi Credibility in social media: Opin- ions, news, and health information—A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery , 5 (2017), e1209

2017
[63]

P , Asadpour

Bazmi. P , Asadpour. M. Shakery. A, Multi-view co-atten tion network for fake news detection by modeling topic-speciﬁc user and n ews source credibility. In Inf. Process. Manag. , 2023, 60, 103146

2023
[64]

Zhang. J, Dong. B, Y u. P .S, Fake News Detection with Deep Diffusive Network Model. In arXiv 2018 , arXiv:1805.08751

2018 arXiv
[65]

K. A. Qureshi, R. A. S. Malick, M. Sabih and H. Cheriﬁ, Com plex Network and Source Inspired COVID-19 Fake News Classiﬁcati on on Twitter. In IEEE Access , vol. 9, pp. 139636-139656, 2021

2021
[66]

In Knowledge-Based Systems , V olume 256, 2022, 109649

Khubaib Ahmed Qureshi, Rauf Ahmed Shams Malick, Muhamm ad Sabih, Hocine Cheriﬁ, Deception detection on social media: A source- based perspective. In Knowledge-Based Systems , V olume 256, 2022, 109649

2022
[67]

Davis, Filippo M enczer, and Alessandro Flammini Online human-bot interactions: Detec tion, esti- mation, and characterization

Onur V arol, Emilio Ferrara, Clayton A. Davis, Filippo M enczer, and Alessandro Flammini Online human-bot interactions: Detec tion, esti- mation, and characterization. In Proceedings of the 11th International AAAI Conference on W eb and Social Media. , 2017

2017
[68]

arXiv:1810.01765

Ramy Baly, Georgi Karadzhov, Dimitar Alexandrov, Jame s Glass, and Preslav Nakov Predicting factuality of reporting and bias o f news media sources. arXiv:1810.01765

arXiv
[71]

Benjamin D. Horne, Jeppe Nørregaard, and Sibel Adalı Di fferent spirals of sameness: A study of content sharing in mainstream and alt ernative media In Proceedings of the International AAAI Conference , V ol. 13. 257–266
[73]

In Proceedings of the web conference , 2 pp

Mingxi Cheng, Shahin Nazarian, Paul Bogdan VRoC: V aria tional Autoencoder-aided Multi-task Rumor Classiﬁer Based on Tex t. In Proceedings of the web conference , 2 pp. 2892- 2898. 2020

2020
[74]

In Scientiﬁc Reports , 11, no

Mingxi Cheng, Yizhi Li, Shahin Nazarian and Paul Bogdan From rumor to genetic mutation detection with explanations: a GAN appr oach. In Scientiﬁc Reports , 11, no. 1 (2021): 5861. 23

2021
[75]

In Proceedings of the W eb Conference, pp

An Lao, Chongyang Shi, Y ayi Y ang Rumor Detection with Fi eld of Linear and Non-Linear Propagation. In Proceedings of the W eb Conference, pp. 3178-3187. 2021

2021
[76]

In Scientiﬁc reports , 11, no

Mingxi Cheng, Chenzhong Yin, Shahin Nazarian and Paul B ogdan Deciphering the laws of social network-transcendent COVID -19 mis- information dynamics and implications for combating misin formation phenomena. In Scientiﬁc reports , 11, no. 1 (2021): 10424

2021
[77]

Zhao, Wenbing Huang, Y u Rong, Junzhou Huang Rumor detection on social media with bi-direc tional graph convolutional networks

Tian Bian, Xi Xiao, Tingyang Xu, P . Zhao, Wenbing Huang, Y u Rong, Junzhou Huang Rumor detection on social media with bi-direc tional graph convolutional networks. In Proceedings of the AAAI conference on artiﬁcial intelligence ,vol. 34, no. 01, pp. 549-556. 2020

2020
[78]

In Academic Journal of Science and Technology ,V ol

Tianrui Liu, Qi Cai, Changxin Xu, Bo Hong, Fanghao Ni, Y u xin Qiao, Tsungwei Y ang Rumor Detection with A Novel Graph Neural Netw ork Approach. In Academic Journal of Science and Technology ,V ol. 10, No. 1, 2024

2024
[79]

In IEEE International Conference on Acoustics, Speech and Signal Processing (ICA SSP), 2023

Liwen Peng, Songlei Jian, Dongsheng Li, Siqi Shen MRML: Multimodal Rumor Detection by Deep Metric Learning. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICA SSP), 2023

2023
[80]

In WWW ’24: Proceedings of the ACM W eb Conference 2024 , 2024

Huaiwen Zhang, Xinxin Liu, Qing Y ang, Y ang Y ang, Fan Qi, Sheng- sheng Qian, Changsheng Xu T3RD: Test-Time Training for Rumo r Detection on Social Media. In WWW ’24: Proceedings of the ACM W eb Conference 2024 , 2024

2024
[81]

Somya Ranjan Sahoo, B. B. Gupta Classiﬁcation of spamme r and nonspammer content in online social network using genetic a lgorithm- based feature selection. In Enterprise Information Systems ,V olume 14, 2020 - Issue 5

2020
[82]

Albert, H

R. Albert, H. Jeong, and A.-L. Barab´ asi Error and attac k tolerance of complex networks In Soc. Nature, vol. 406, no. 6794, pp. 378–382, Jul. 2000

2000
[83]

Freeman Centrality in social networks conceptual clariﬁcation In Soc

L.C. Freeman Centrality in social networks conceptual clariﬁcation In Soc. Netw., (1978) 215–239

1978
[84]

Holme, B.J

P . Holme, B.J. Kim, C.N. Y oon, S.K. Han, Attack vulnerab ility of complex networks In Phys. Rev. E , 65 (2002) 56109

2002
[85]

Liu, J.J

Y .Y . Liu, J.J. Slotine, A.L. Barabasi, Controllability of complex networks In Nature, Nature 473 (2011) 167–173

2011
[86]

Bonacich, Power and centrality: A family of measures In Amer

P . Bonacich, Power and centrality: A family of measures In Amer . J. Sociol, vol. 92, no. 5, pp. 1170–1182, 1987

1987
[87]

M. E. Newman, The mathematics of networks In The New Palgrave Encyclopedia of Economics, vol. 2. Basingstoke, U.K.: Palgrave Macmil- lan, 2008, pp. 1–12

2008
[88]

M. Li, X. Wang, K. Gao, S. Zhang, LA survey on information diffusion in online social networks: models and methods. Information8 (2017) 118

2017
[89]

Proceedings of the ninth ACM SIGKDD 2003

David Kempe, Jon Kleinberg, ´Eva Tardos Maximizing the spread of inﬂuence through a social network. Proceedings of the ninth ACM SIGKDD 2003. Pages 137–146

2003
[90]

ACM SIGMETRICS Performance Evaluation Review V olume

Jungseul Ok, Y oungmi Jin, Jinwoo Shin, Y ung Yi On maximi zing diffusion speed in social networks: impact of random seedin g and clustering. ACM SIGMETRICS Performance Evaluation Review V olume
[91]

pp 301–313

Issue 1. pp 301–313. 16 June 2014

2014
[92]

Hu, Z. Shen, Z. Cao, S. Podobnik, B. Y ang, H. Wang, W. Loca ting multiple diffusion sources in time varying networks from sp arse obser- vations. Nat. Sci. Rep. 2018, 8, 2685

2018
[93]

Detecting Sources of Computer Virus es in Networks: Theory and Experiment

Shah, D., Zaman, T. Detecting Sources of Computer Virus es in Networks: Theory and Experiment. In Proceedings of the ACM S IG- METRICS International Conference on Measurement and Model ing of Computer Systems, New Y ork, NY , USA, 14–18 June 2010

2010
[94]

Rumor Centrality: A Universal Sourc e Detector

Shah, D., Zaman, T. Rumor Centrality: A Universal Sourc e Detector. In Proceedings of the 12th ACM SIGMETRICS/PERFORMANCE Joint International Conference on Measurement and Modeling of Co mputer Systems, London, UK, 12–14 June 2012

2012
[95]

Rumors in a Network: Who’s the Culpri t? IEEE Trans

Shah, D., Zaman, T. Rumors in a Network: Who’s the Culpri t? IEEE Trans. Inf. Theory 2011, 57, 5163–5181

2011
[96]

Information Source Detection in the SI R Model: A Sample Path Based Approach

Zhu, K., Ying, L. Information Source Detection in the SI R Model: A Sample Path Based Approach. In Proceedings of the IEEE Infor mation Theory and Applications Workshop (ITA), San Diego, CA, USA, 10–15 February 2013

2013
[97]

A robust information source estimator with sparse observations

Zhu, K., Ying, L. A robust information source estimator with sparse observations. In Proceedings of the IEEE INFOCOM, Toronto, ON, Canada, 27 April–2 May 2014

2014
[98]

Rumor source det ection with multiple observations: Fundamental limits and algori thms

Wang, Z., Dong, W., Zhang, W., Tan, C.W. Rumor source det ection with multiple observations: Fundamental limits and algori thms. In Proceedings of the ACM SIGMETRICS, Austin, TX, USA, 16–20 Ju ne 2014

2014
[100]

InOnline Social Networks and Media

Sushila Shelke, V ahida Attar Source detection of rumor in social network A review. InOnline Social Networks and Media. 9 (2019) 30–42

2019
[101]

Information Source Localiza tion with Protector Diffusion in Networks

Choi, J., Shin, J., Yi, Y . Information Source Localiza tion with Protector Diffusion in Networks. IEEE/KICS J. Commun. Netw. 2017, 21, 136– 147

2017
[102]

Rum or Source Detection under Querying with Untruthful Answers

Choi, J., Moon, S., Woo, J., Son, K., Shin, J., Yi, Y . Rum or Source Detection under Querying with Untruthful Answers. In Proce edings of the IEEE INFOCOM, Atlanta, GA, USA, 1–4 May 2017

2017
[103]

Necessary and Sufﬁcient Budgets in Inf ormation Source Finding with Querying: Adaptivity Gap

Choi, J., Yi, Y . Necessary and Sufﬁcient Budgets in Inf ormation Source Finding with Querying: Adaptivity Gap. In Proceedings of th e ISIT, V ail, CO, USA, 17–22 June 2018

2018
[104]

Time varying net works and the weakness of strong ties

Karsai, M., Perra, N., V espignani, A. Time varying net works and the weakness of strong ties. Sci. Rep. 2014, 4, 4001

2014
[105]

Rumor Sou rce Identiﬁcation in Social Networks with Time-V arying Topolo gy

Jiang, J., Wen, S., Y u, S., Xiang, Y ., Zhou, W. Rumor Sou rce Identiﬁcation in Social Networks with Time-V arying Topolo gy. IEEE Trans. Dependable Secur . Comput. 2018, 15, 166–179

2018
[106]

Information Source Detection in Netw orks: Possi- bility and Impossible Results

Zhu, K., Ying, L. Information Source Detection in Netw orks: Possi- bility and Impossible Results. In Proceedings of the IEEE IN FOCOM, San Francisco, CA, USA, 10–15 April 2016

2016
[107]

Spotting Cu lprits in Epi- demics: How Many and Which Ones? In Proceedings of the IEEE International Conference on Data Mining, Brussels, Belgiu m, 10–13 December 2012

Prakash, B.A., Vreeken, J., Faloutsos, C. Spotting Cu lprits in Epi- demics: How Many and Which Ones? In Proceedings of the IEEE International Conference on Data Mining, Brussels, Belgiu m, 10–13 December 2012

2012
[108]

Predicting the S ources of an Outbreak with a Spectral Technique

Fioriti, V ., Chinnici, M., Palomo, J. Predicting the S ources of an Outbreak with a Spectral Technique. Appl. Math. Sci. 2014, 8, 6775– 6782

2014
[109]

Finding Adam in ran dom growing trees

Bubeck, S., Devroye, L., Lugosi, G. Finding Adam in ran dom growing trees. Random Struct. Algorithms 2017, 50, 158–172

2017
[110]

Conﬁdence Sets for Source of a Diff usion in Regular Trees

Khim, J., Loh, P .-L. Conﬁdence Sets for Source of a Diff usion in Regular Trees. IEEE Trans. Netw. Sci. Eng. 2015, 4, 27–40

2015
[111]

How to Identify an Infectio n Source With Limited Observations

Luo, W., Tay, W.P ., Leng, M. How to Identify an Infectio n Source With Limited Observations. IEEE J. Sel. Top. Signal Process. 2014, 8, 586–597

2014
[112]

Sundareisan, J

S. Sundareisan, J. Vreeken, and B. Aditya Prakash Hidd en Hazards: Finding Missing Nodes in Large Graph Epidemics. Proceedings of the 2015 SIAM International Conference on Data Mining. Pages 415 - 423, 2015

2015
[113]

IEEE International Symposium on Information Theory

Nikhil Karamchandani; Massimo Franceschetti Rumor s ource detec- tion under probabilistic sampling. IEEE International Symposium on Information Theory. July 2013

2013
[114]

Journal of Statistical Mechanics: Theory and Experiment

Fabrizio Altarelli, Alfredo Braunstein, Luca Dall’A sta1, Alessandro Ingrosso1 and Riccardo Zecchina The patient-zero problem w ith noisy observations. Journal of Statistical Mechanics: Theory and Experiment. V olume 2014, October 2014

2014
[115]

ACM Transactions on Internet Technology

Peng Zhang, Jing He, Guodong Long, Guangyan Huang, Che ngqi Zhang Towards Anomalous Diffusion Sources Detection in a La rge Network. ACM Transactions on Internet Technology. V ol. 16, Article No.: 2pp 1–24, 2016

2016
[116]

Journal of Combinatorial Optimization V olume 33, pages 132–146, (2017)

Zhao Zhang, Wen Xu, Weili Wu and Ding-Zhu Du A novel ap- proach for detecting multiple rumor sources in networks wit h partial observations. Journal of Combinatorial Optimization V olume 33, pages 132–146, (2017)

2017
[117]

SPIE Optical Engineering 2019, San Diego, California, United States

Roxana Alexandru, Pier Luigi Dragotti Diffusion sour ce detection in a network using partial observations. SPIE Optical Engineering 2019, San Diego, California, United States

2019
[118]

ACM International Conference on Information and Knowledge Management November 2019

Ming Dong, Bolong Zheng, Nguyen Quoc Viet Hung, Han Su, Guohui Li Multiple Rumor Source Detection with Graph Convolutiona l Net- works. ACM International Conference on Information and Knowledge Management November 2019. Pages 569–578

2019
[119]

Malicious Attack Propagation and Source Identiﬁcation November 2018

Jiaojiao Jiang, Sheng Wen, Shui Y u, Bo Liu, Y ang Xiang, and Wanlei Zhou Identifying Multiple Propagation Sources. Malicious Attack Propagation and Source Identiﬁcation November 2018

2018
[120]

IEEE Journal of Selected Topics in Signal Processing V olume: 9, Issue: 4, Page(s): 663 - 677, June 2015

Zhaoxu Wang; Wenxiang Dong; Wenyi Zhang; Chee Wei Tan R ooting our Rumor Sources in Online Social Networks: The V alue of Div ersity From Multiple Observations. IEEE Journal of Selected Topics in Signal Processing V olume: 9, Issue: 4, Page(s): 663 - 677, June 2015

2015
[121]

Efﬁciently Spotting the Starting Points of an Epidemic in a Large Graph

Prakash, B.A., Vreeken, J., Faloutsos, C. Efﬁciently Spotting the Starting Points of an Epidemic in a Large Graph. In Proceedin gs of the ICDM, New Orleans, LA, USA, 18–21 November 2017

2017
[122]

Catch’Em All: Locating Mult iple Diffusion Sources in Networks with Partial Observations

Zhu, K., Chen, Z., Ying, L. Catch’Em All: Locating Mult iple Diffusion Sources in Networks with Partial Observations. In Proceedi ngs of the AAAI, San Francisco, CA, USA, 4–9 February 2017

2017
[123]

An Algorithmic Framework for Estimat ing Rumor Sources with Different Start Times

Ji, F., Tay, W.P . An Algorithmic Framework for Estimat ing Rumor Sources with Different Start Times. IEEE Trans. Signal Process. 2017, 65, 2517–2530

2017
[124]

Rumor source detection for rumo r spreading on random increasing trees

Michael, F., Y u, P .-D. Rumor source detection for rumo r spreading on random increasing trees. Electron. Commun. Probab. 2015, 20, 2

2015
[125]

Spinelli, L

B. Spinelli, L. Elisa Celis, P . Thiran A General Framew ork for Sensor Placement in Source Localization. IEEE Transactions on Network Science and Engineering V olume: 6, Issue: 2, 01 April-June 2019

2019
[126]

P . C. Pinto, P . Thiran, and M. V etterli Locating the sou rce of diffusion in large-scale networks. Phys. Rev. Lett vol. 109, no. 6, Aug. 2012, Art. no. 068702

2012
[127]

Agaskar and Y

A. Agaskar and Y . M. Lu, A fast Monte Carlo algorithm for source localization on graphs. Proc. SPIE Opt. Eng. Appl San Diego, CA, USA, 2013 Art. no. 068702. 24

2013
[129]

E. Seo, P . Mohapatra, and T. Abdelzaher Identifying ru mors and their sources in social networks. In Proc. SPIE Defense Security Sens., USA, 2012

2012
[130]

Scientiﬁc Reports 13, Article number: 5692 (2023)

Hong-Jue Wang, Zhao-Long Hu, Li Tao, Shuyu Shao and Shi -Zhe Wang The locatability of Pearson algorithm for multi-sourc e location in complex networks. Scientiﬁc Reports 13, Article number: 5692 (2023)

2023
[131]

Scientiﬁc Reports 12, Article number: 5467 (2022)

Y anXia Liu, WeiMin Li, Chao Y ang and JianJia Wang Multi -source detection based on neighborhood entropy in social networks . Scientiﬁc Reports 12, Article number: 5467 (2022)

2022
[132]

Data Analysis and Knowledge Discovery 2021, V ol

Chen Yixin,Chen Xinyue,Liu Yi,Wang Hanzhen,Lai Y ong qing,Xu Y ang Detecting Rumor Dissemination and Sources with SIDR Mo del. Data Analysis and Knowledge Discovery 2021, V ol. 5 Issue (1): 78-89

2021
[133]

Preprint submitted to Information Sciences February 14, 2024

Da-Wen Huanga, Wenjie Wua, Jichao Bib, Junli Lia, Chen quan Ganc, Wei Zhoud Timeliness-aware multiple rumor sources estimat ion in dynamic online social networks. Preprint submitted to Information Sciences February 14, 2024

2024
[136]

E. Seo, P . Mohapatra, T. Abdelzaher Identifying rumor s and their sources in social networks. SPIE Defense, Security, and Sensing 24 May 2012

2012
[137]

IEEE Transactions on Knowledge and Data Engineering Nov

Ranran Wang, Yin Zhang, Wenchao Wan, Min Chen and Mohse n Guizani Distributed Rumor Source Detection via Boosted Fed erated Learning. IEEE Transactions on Knowledge and Data Engineering Nov. 2024, pp. 5986-6001, vol. 36

2024
[138]

StegHash: New Method for Informatio n Hiding in Open Social Networks In IJET International Journal of Electronics and Telecommunication, 62 (4) : 347–352

Szczypiorski, K. StegHash: New Method for Informatio n Hiding in Open Social Networks In IJET International Journal of Electronics and Telecommunication, 62 (4) : 347–352
[139]

SocialStegDisc: Appl ication of steganog- raphy in social networks to create a ﬁle system In In Proc

Bieniasz, J., Szczypiorski, K. SocialStegDisc: Appl ication of steganog- raphy in social networks to create a ﬁle system In In Proc. of 3rd International Conference on Frontiers of Signal Processin g (ICFSP 2017), Paris, France, 6-8 September 2017

2017
[140]

Fanti, P

G. Fanti, P . Kairouz, S. Oh and P . Viswanath. Spy vs. Spy : Rumor Source Obfuscation. In Proceedings of ACM SIGMETRICS , 2015

2015
[141]

Fanti, P

G. Fanti, P . Kairouz, S. Oh, K. Ramchandran and P . Viswa nath. Rumor Source Obfuscation on Irregular Trees. In Proceedings of ACM SIGMETRICS, 2016

2016
[142]

Fanti, P

G. Fanti, P . Kairouz, S. Oh, K. Ramchandran, P . Viswana th Hiding the Rumor Source. In IEEE Transactions on Information Theory , V olume: 63, Issue: 10, Page(s): 6679 - 6713, October 2017

2017
[143]

W. Luo, W. P . Tay and M. Leng. Infection Spreading and So urce Identiﬁcation: A Hide and Seek Game. IEEE Transactions on Signal Processing, vol. 64, no. 16, Aug. 15, 2016

2016
[145]

Y . Liu, H. Shen, L. Shi A review of rumor detection techn iques in social networks. Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology , V olume 44 Issue 3 2023 pp 3561–3578

2023
[146]

A. Imad E. Hosni, K. Li, S. Ahmad Minimizing rumor inﬂue nce in multiplex online social networks based on human individual and social behaviors. Information Sciences , V olume 512, February 2020, Pages 1458-1480

2020
[147]

Shelke and V

S. Shelke and V . Attar Rumor detection in social networ k based on user, content and lexical features. Multimedia Tools and Applications , V olume 81, pages 17347–17368, (2022)

2022
[148]

Zafarani, H

R. Zafarani, H. Liu Evaluation without ground truth in social media research. Communications of the ACM , V olume 58 Issue 6 pp 54–60, 2015

2015
[149]

Gelardi, D

V . Gelardi, D. Le Bail, A. Barrat and N. Claidiere From t emporal network data to the dynamics of social relationships. Proceedings B is the Royal Society , Published:29 September 2021

2021
[150]

S. Kwon, M. Cha ,K. Jung Rumor Detection over V arying Ti me Windows. PLOS ONE , January 12, 2017

2017
[151]

Chang, X

Q. Chang, X. Li, Z. Duan A novel approach for rumor detec tion in social platforms: Memory-augmented transformer with grap h convolu- tional networks. Knowledge-Based Systems, V olume 292, 23 May 2024, 111625

2024
[152]

Teja Peddinti, K

S. Teja Peddinti, K. W. Ross, J. Cappos ”On the internet , nobody knows you’re a dog”: a twitter case study of anonymity in social net works. Proceedings of the second ACM conference on Online social ne tworks, October 2014. Pages 83–94

2014
[153]

Choi Epidemic Source Detection over Dynamic Networ ks

J. Choi Epidemic Source Detection over Dynamic Networ ks. Elec- tronics, 19 June 2020

2020
[154]

B¨ ohm and S

I. B¨ ohm and S. Lolagar Open source intelligence. International Cybersecurity Law Review , V olume 2, pages 317–337, (2021)

2021
[155]

Zhang, Y

Y . Zhang, Y . Su, Li W., Haiou Liu Interacting model of ru mor propagation and behavior spreading in multiplex networks. Chaos, Solitons and Fractals , V olume 121, April 2019, Pages 168-177

2019
[156]

Y ang, J

Y . Y ang, J. Zhao, S. Wang, X. Min, P . Wang, H. Wang Multim odal Short Video Rumor Detection System Based on Contrastive Lea rning. arXiv:2304.08401 , 2023

2023 arXiv
[157]

C. Wang; B. Zhou; H. Tu; Y . Liu Rumor Detection on Social Media Using Temporal Dynamic Structure and Emotional Informatio n. 2021 IEEE Sixth International Conference on Data Science in Cybe rspace (DSC) , 09-11 October 2021

2021
[158]

W. Chen, Y . Zhang, C. Kiat Y eo, C. Tong Lau, B. Sung Lee Un super- vised rumor detection based on users’ behaviors using neura l networks. Pattern Recognition Letters , V olume 105, 1 April 2018, Pages 226-233

2018
[159]

Y . Lyu, X. Y ang, J. Liu, P . S. Y u, S. Xie, X. Zhang Interpr etable and Effective Reinforcement Learning for Attacking against Gr aph-based Rumor Detection. arXiv:2201.05819 , 2022

2022 arXiv

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[1] [128]

Altarelli, A

F. Altarelli, A. Braunstein, L. Dall’Asta, A. Lage-Ca stellanos, and R. Zecchina, Bayesian inference of epidemics on networks vi a belief propagation. Phys. Rev. Lett vol. 112, no. 11, 2014, Art. no. 118701

work page 2014

[2] [135]

IEEE Confer- ence on Computer Communications 2020

Shan Qu, Ziqi Zhao, Luoyi Fu, Xinbing Wang, Jun Xu Joint Inference on Truth/Rumor and Their Sources in Social Networks. IEEE Confer- ence on Computer Communications 2020

work page 2020

[3] [134]

Expert Systems With Applications February 16, 2024

Ravi Kishore Devarapalli, Anupam Biswas Estimating r umor source in social networks using incomplete observer information. Expert Systems With Applications February 16, 2024

work page 2024

[4] [69]

Dennis Fetterly, Mark Manasse, and Marc Najork Detecti ng phrase-level duplication on the World Wide Web In Proceedings of the 28th Annual International ACM SIGIR Conference , New Y ork, NY , 170–177.2005

2005

[5] [70]

Alexandros Ntoulas, Marc Najork, Mark Manasse, and Den nis Fetterly Detecting spam web pages through content analysis In Proceedings of the 15th International Conference on W orld Wide W eb , ACM, New Y ork, NY , 83–92

[6] [72]

In Machine Learning, 2 (2010), 207–225

Jacob Abernethy, Olivier Chapelle, and Carlos Castill o Graph regular- ization methods for web spam detection. In Machine Learning, 2 (2010), 207–225

2010

[7] [3]

In arXiv:1807.03505

Juan Cao, Junbo Guo, Xirong Li, Zhiwei Jin, Han Guo, Jinta o Li Auto- matic Rumor Detection on Microblogs: A Survey. In arXiv:1807.03505

arXiv

[8] [7]

Alzanin a, Aqil M

Samah M. Alzanin a, Aqil M. Azmi Detecting rumors in socia l media: A survey. In Procedia Computer Science , V olume 142, 2018, Pages 294-300

2018

[9] [8]

Rumor Detection on Social Media: Datasets, Methods and Opportunities

Quanzhi Li, Qiong Zhang, Luo Si, Yingchi Liu Rumor Detec- tion on Social Media: Datasets, Methods and Opportunities. In arxiv/papers/1911/1911.07199 ,

work page Pith review arXiv 1911

[10] [9]

Wen, Shui Y u, Y

Jiaojiao Jiang, S. Wen, Shui Y u, Y . Xiang, Wanlei Zhou Ide ntifying Propagation Sources in Networks: State-of-the-Art and Com parative Studies. In IEEE Communications Surveys and Tutorials , VOL. 19, NO. 1, FIRST QUARTER 2017

2017

[11] [12]

Sharma Rumors detec tion, veriﬁcation and controlling mechanisms in online social ne tworks: A survey

Mohammad Ahsan, Madhu Kumari, T.P . Sharma Rumors detec tion, veriﬁcation and controlling mechanisms in online social ne tworks: A survey. In Online Social Networks and Media , V olume 14, November 2019, 100050

2019

[12] [25]

Zhou and R

X. Zhou and R. Zafarani A Survey of Fake News: Fundamenta l Theories, Detection Methods, and Opportunities. In ACM Computing Surveys , V olume 53 Issue 5 Article No.: 109 pp 1–40

[13] [26]

In Applied sciences , 2023, 13, 11877

Y anping Shen, Qingjie Liu, Na Guo , Jing Y uan and Y anqing Y ang Fake News Detection on Social Networks: A Survey. In Applied sciences , 2023, 13, 11877

2023

[14] [99]

Rooting Out the Rumor Culp rit from Suspects

Dong, W., Zhang, W., Tan, C.W. Rooting Out the Rumor Culp rit from Suspects. In Proceedings of the IEEE International Symposi um on Information Theory (ISIT), Istanbul, Turkey, 7–12 July 201 3

work page

[15] [144]

Bazzaz Abkenar, M

S. Bazzaz Abkenar, M. Haghi Kashani, E. Mahdipour, S. M ahdi Jameii Big data analytics meets social media: A systematic r eview of techniques, open issues, and future directions. Telematics and Informatics, V olume 57, March 2021, 101517

work page 2021