Disinformation refers to false rumors deliberately fabricated for certain political or economic conspiracies. So far, how to prevent online disinformation propagation is still a severe challenge. Refutation, media censorship, and social bot detection are three popular approaches to stopping disinformation, which aim to clarify facts, intercept the spread of existing disinformation, and quarantine the source of disinformation, respectively. In this paper, we study the collaboration of the above three countermeasures in defending disinformation. Specifically, considering an online social network, we study the most cost-effective dynamic budget allocation (DBA) strategy for the three methods to minimize the proportion of disinformation-supportive accounts on the network with the lowest expenditure. For convenience, we refer to the search for the optimal DBA strategy as the DBA problem. Our contributions are as follows. First, we propose a disinformation propagation model to characterize the effects of different DBA strategies on curbing disinformation. On this basis, we establish a trade-off model for DBA strategies and reduce the DBA problem to an optimal control model. Second, we derive an optimality system for the optimal control model and develop a heuristic numerical algorithm called the DBA algorithm to solve the optimality system. With the DBA algorithm, we can find possible optimal DBA strategies. Third, through numerical experiments, we estimate key model parameters, examine the obtained DBA strategy, and verify the effectiveness of the DBA algorithm. Results show that the DBA algorithm is effective.
Citation: Yi Wang, Shicheng Zhong, Guo Wang. Preventing online disinformation propagation: Cost-effective dynamic budget allocation of refutation, media censorship, and social bot detection[J]. Mathematical Biosciences and Engineering, 2023, 20(7): 13113-13132. doi: 10.3934/mbe.2023584
[1] | Muajebah Hidan, Abbas Kareem Wanas, Faiz Chaseb Khudher, Gangadharan Murugusundaramoorthy, Mohamed Abdalla . Coefficient bounds for certain families of bi-Bazilevič and bi-Ozaki-close-to-convex functions. AIMS Mathematics, 2024, 9(4): 8134-8147. doi: 10.3934/math.2024395 |
[2] | Abdulmtalb Hussen, Mohammed S. A. Madi, Abobaker M. M. Abominjil . Bounding coefficients for certain subclasses of bi-univalent functions related to Lucas-Balancing polynomials. AIMS Mathematics, 2024, 9(7): 18034-18047. doi: 10.3934/math.2024879 |
[3] | Tariq Al-Hawary, Ala Amourah, Abdullah Alsoboh, Osama Ogilat, Irianto Harny, Maslina Darus . Applications of q−Ultraspherical polynomials to bi-univalent functions defined by q−Saigo's fractional integral operators. AIMS Mathematics, 2024, 9(7): 17063-17075. doi: 10.3934/math.2024828 |
[4] | Abeer O. Badghaish, Abdel Moneim Y. Lashin, Amani Z. Bajamal, Fayzah A. Alshehri . A new subclass of analytic and bi-univalent functions associated with Legendre polynomials. AIMS Mathematics, 2023, 8(10): 23534-23547. doi: 10.3934/math.20231196 |
[5] | Bilal Khan, H. M. Srivastava, Muhammad Tahir, Maslina Darus, Qazi Zahoor Ahmad, Nazar Khan . Applications of a certain q-integral operator to the subclasses of analytic and bi-univalent functions. AIMS Mathematics, 2021, 6(1): 1024-1039. doi: 10.3934/math.2021061 |
[6] | Sheza. M. El-Deeb, Gangadharan Murugusundaramoorthy, Kaliyappan Vijaya, Alhanouf Alburaikan . Certain class of bi-univalent functions defined by quantum calculus operator associated with Faber polynomial. AIMS Mathematics, 2022, 7(2): 2989-3005. doi: 10.3934/math.2022165 |
[7] | Luminiţa-Ioana Cotîrlǎ . New classes of analytic and bi-univalent functions. AIMS Mathematics, 2021, 6(10): 10642-10651. doi: 10.3934/math.2021618 |
[8] | F. Müge Sakar, Arzu Akgül . Based on a family of bi-univalent functions introduced through the Faber polynomial expansions and Noor integral operator. AIMS Mathematics, 2022, 7(4): 5146-5155. doi: 10.3934/math.2022287 |
[9] | Norah Saud Almutairi, Adarey Saud Almutairi, Awatef Shahen, Hanan Darwish . Estimates of coefficients for bi-univalent Ma-Minda-type functions associated with q-Srivastava-Attiya operator. AIMS Mathematics, 2025, 10(3): 7269-7289. doi: 10.3934/math.2025333 |
[10] | Tingting Du, Zhengang Wu . Some identities involving the bi-periodic Fibonacci and Lucas polynomials. AIMS Mathematics, 2023, 8(3): 5838-5846. doi: 10.3934/math.2023294 |
Disinformation refers to false rumors deliberately fabricated for certain political or economic conspiracies. So far, how to prevent online disinformation propagation is still a severe challenge. Refutation, media censorship, and social bot detection are three popular approaches to stopping disinformation, which aim to clarify facts, intercept the spread of existing disinformation, and quarantine the source of disinformation, respectively. In this paper, we study the collaboration of the above three countermeasures in defending disinformation. Specifically, considering an online social network, we study the most cost-effective dynamic budget allocation (DBA) strategy for the three methods to minimize the proportion of disinformation-supportive accounts on the network with the lowest expenditure. For convenience, we refer to the search for the optimal DBA strategy as the DBA problem. Our contributions are as follows. First, we propose a disinformation propagation model to characterize the effects of different DBA strategies on curbing disinformation. On this basis, we establish a trade-off model for DBA strategies and reduce the DBA problem to an optimal control model. Second, we derive an optimality system for the optimal control model and develop a heuristic numerical algorithm called the DBA algorithm to solve the optimality system. With the DBA algorithm, we can find possible optimal DBA strategies. Third, through numerical experiments, we estimate key model parameters, examine the obtained DBA strategy, and verify the effectiveness of the DBA algorithm. Results show that the DBA algorithm is effective.
Let A indicate an analytic functions family, which is normalized under the condition f (0)= f′(0)−1=0 in U={z:z∈C and |z |<1} and given by the following Taylor-Maclaurin series:
f (z)=z+∞∑n=2anzn . | (1.1) |
Further, by S we shall denote the class of all functions in A which are univalent in U.
With a view to recalling the principle of subordination between analytic functions, let the functions f and g be analytic in U. Then we say that the function f is subordinate to g if there exists a Schwarz function w(z), analytic in U with
ω(0)=0, |ω(z)|<1, (z∈U) |
such that
f (z)=g (ω(z)). |
We denote this subordination by
f≺g or f (z)≺g (z). |
In particular, if the function g is univalent in U, the above subordination is equivalent to
f (0)=g (0), f (U)⊂g (U). |
The Koebe-One Quarter Theorem [11] asserts that image of U under every univalent function f∈A contains a disc of radius 14. thus every univalent function f has an inverse f−1 satisfying f−1(f(z))=z and f ( f−1 (w))=w (|w|<r 0(f ),r 0(f ) >14 ), where
f−1(w)=w−a2w2+(2a22−a3)w3−(5a32−5a2a3+a4)w4+⋯. | (1.2) |
A function f∈A is said to be bi-univalent functions in U if both f and f−1 are univalent in U. A function f∈S is said to be bi-univalent in U if there exists a function g∈S such that g(z) is an univalent extension of f−1 to U. Let Λ denote the class of bi-univalent functions in U. The functions z1−z, −log(1−z), 12log(1+z1−z) are in the class Λ (see details in [20]). However, the familiar Koebe function is not bi-univalent. Lewin [17] investigated the class of bi-univalent functions Λ and obtained a bound |a2|≤1.51. Motivated by the work of Lewin [17], Brannan and Clunie [9] conjectured that |a2|≤√2. The coefficient estimate problem for |an|(n∈N,n≥3) is still open ([20]). Brannan and Taha [10] also worked on certain subclasses of the bi-univalent function class Λ and obtained estimates for their initial coefficients. Various classes of bi-univalent functions were introduced and studied in recent times, the study of bi-univalent functions gained momentum mainly due to the work of Srivastava et al. [20]. Motivated by this, many researchers [1], [4,5,6,7,8], [13,14,15], [20], [21], and [27,28,29], also the references cited there in) recently investigated several interesting subclasses of the class Λ and found non-sharp estimates on the first two Taylor-Maclaurin coefficients. Recently, many researchers have been exploring bi-univalent functions, few to mention Fibonacci polynomials, Lucas polynomials, Chebyshev polynomials, Pell polynomials, Lucas–Lehmer polynomials, orthogonal polynomials and the other special polynomials and their generalizations are of great importance in a variety of branches such as physics, engineering, architecture, nature, art, number theory, combinatorics and numerical analysis. These polynomials have been studied in several papers from a theoretical point of view (see, for example, [23,24,25,26,27,28,29,30] also see references therein).
We recall the following results relevant for our study as stated in [3].
Let p(x) and q(x) be polynomials with real coefficients. The (p,q)− Lucas polynomials Lp,q,n(x) are defined by the recurrence relation
Lp,q,n(x)=p(x)Lp,q,n−1(x)+q(x)Lp,q,n−2(x)(n≥2), |
from which the first few Lucas polynomials can be found as
Lp,q,0(x)=2,Lp,q,1(x)=p(x),Lp,q,2(x)=p2(x)+2q(x),Lp,q,3(x)=p3(x)+3p(x)q(x),.... | (1.3) |
For the special cases of p(x) and q(x), we can get the polynomials given Lx,1,n(x)≡Ln(x) Lucas polynomials, L2x,1,n(x)≡Dn(x) Pell–Lucas polynomials, L1,2x,n(x)≡jn(x) Jacobsthal–Lucas polynomials, L3x,−2,n(x)≡Fn(x) Fermat–Lucas polynomials, L2x,−1,n(x)≡Tn(x) Chebyshev polynomials first kind.
Lemma 1.1. [16] Let G{L(x)}(z)be the generating function of the (p,q)−Lucas polynomial sequence Lp,q,n(x).Then,
G{L(x)}(z)=∞∑n=0Lp,q,n(x)zn=2−p(x)z1−p(x)z−q(x)z2 |
and
G{L(x)}(z)=G{L(x)}(z)−1=1+∞∑n=1Lp,q,n(x)zn=1+q(x)z21−p(x)z−q(x)z2. |
Definition 1.2. [22] For ϑ≥0, δ∈R, ϑ+iδ≠0 and f∈A, let B(ϑ,δ) denote the class of Bazilevič function if and only if
Re[(zf′(z)f(z))(f(z)z)ϑ+iδ]>0. |
Several authors have researched different subfamilies of the well-known Bazilevič functions of type ϑ from various viewpoints (see [3] and [19]). For Bazilevič functions of order ϑ+iδ, there is no much work associated with Lucas polynomials in the literature. Initiating an exploration of properties of Lucas polynomials associated with Bazilevič functions of order ϑ+iδ is the main goal of this paper. To do so, we take into account the following definitions. In this paper motivated by the very recent work of Altinkaya and Yalcin [3] (also see [18]) we define a new class B(ϑ,δ), bi-Bazilevič function of Λ based on (p,q)− Lucas polynomials as below:
Definition 1.3. For f∈Λ, ϑ≥0, δ∈R, ϑ+iδ≠0 and let B(ϑ,δ) denote the class of Bi-Bazilevič functions of order t and type ϑ+iδ if only if
[(zf′(z)f(z))(f(z)z)ϑ+iδ]≺G{L(x)}(z)(z∈U) | (1.4) |
and
[(zg′(w)g(w))(g(w)w)ϑ+iδ]≺G{L(x)}(w)(w∈U), | (1.5) |
where GLp,q,n(z)∈Φ and the function g is described as g(w)=f−1(w).
Remark 1.4. We note that for δ=0 the class R(ϑ,0)=R(ϑ) is defined by Altinkaya and Yalcin [2].
The class B(0,0)=S∗Λ is defined as follows:
Definition 1.5. A function f∈Λ is said to be in the class S∗Λ, if the following subordinations hold
zf′(z)f(z)≺G{L(x)}(z)(z∈U) |
and
wg′(w)g(w)≺G{L(x)}(w)(w∈U) |
where g(w)=f−1(w).
We begin this section by finding the estimates of the coefficients |a2| and |a3| for functions in the class B(ϑ,δ).
Theorem 2.1. Let the function f(z) given by 1.1 be in the class B(ϑ,δ). Then
|a2|≤p(x)√2p(x)√|{((ϑ+iδ)2+3(ϑ+iδ)+2)−2(ϑ+iδ+1)2}p2(x)−4q(x)(ϑ+iδ+1)2|. |
and
|a3|≤p2(x)(ϑ+1)2+δ2+p(x)√(ϑ+2)2+δ2. |
Proof. Let f∈B(ϑ,δ,x) there exist two analytic functions u,v:U→U with u(0)=0=v(0), such that |u(z)|<1, |v(w)|<1, we can write from (1.4) and (1.5), we have
[(zf′(z)f(z))(f(z)z)ϑ+iδ]=G{L(x)}(z)(z∈U) | (2.1) |
and
[(zg′(w)g(w))(g(w)w)ϑ+iδ]=G{L(x)}(w)(w∈U), | (2.2) |
It is fairly well known that if
|u(z)|=|u1z+u2z2+⋯|<1 |
and
|v(w)|=|v1w+v2w2+⋯|<1. |
then
|uk|≤1and|vk|≤1(k∈N) |
It follows that, so we have
G{L(x)}(u(z))=1+Lp,q,1(x)u(z)+Lp,q,2(x)u2(z)+…=1+Lp,q,1(x)u1z+[Lp,q,1(x)u2+Lp,q,2(x)u21]z2+… | (2.3) |
and
G{L(x)}(v(w))=1+Lp,q,1(x)v(w)+Lp,q,2(x)v2(w)+…=1+Lp,q,1(x)v1w+[Lp,q,1(x)v2+Lp,q,2(x)v21]w2+… | (2.4) |
From the equalities (2.1) and (2.2), we obtain that
[(zf′(z)f(z))(f(z)z)ϑ+iδ]=1+Lp,q,1(x)u1z+[Lp,q,1(x)u2+Lp,q,2(x)u21]z2+…, | (2.5) |
and
[(zg′(w)g(w))(g(w)w)ϑ+iδ]=1+Lp,q,1(x)v1w+[Lp,q,1(x)v2+Lp,q,2(x)v21]w2+…, | (2.6) |
It follows from (2.5) and (2.6) that
(ϑ+iδ+1)a2=Lp,q,1(x)u1,, | (2.7) |
(ϑ+iδ−1)(ϑ+iδ+2)2a22−(ϑ+iδ+2)a3=Lp,q,1(x)u2+Lp,q,2(x)u21, | (2.8) |
and
−(ϑ+iδ+1)a2=Lp,q,1(x)v1, | (2.9) |
(ϑ+iδ+2)(ϑ+iδ+3)2a22+(ϑ+iδ+2)a3=Lp,q,1(x)v2+Lp,q,2(x)v21, | (2.10) |
From (2.7) and (2.9)
u1=−v1 | (2.11) |
and
2(ϑ+iδ+1)2a22=L2p,q,1(x)(u21+v21)., | (2.12) |
by adding (2.8) to (2.10), we get
((ϑ+iδ)2+3(ϑ+iδ)+2)a22=Lp,q,1(x)(u2+v2)+Lp,q,2(x)(u21+v21), | (2.13) |
by using (2.12) in equality (2.13), we have
[((ϑ+iδ)2+3(ϑ+iδ)+2)−2Lp,q,2(x)(ϑ+iδ+1)2L2p,q,1(x)]a22=Lp,q,1(x)(u2+v2), |
a22=L3p,q,1(x)(u2+v2)[((ϑ+iδ)2+3(ϑ+iδ)+2)L2p,q,1(x)−2Lp,q,2(x)(ϑ+iδ+1)2]. | (2.14) |
Thus, from (1.3) and (2.14) we get
|a2|≤p(x)√2p(x)√|{((ϑ+iδ)2+3(ϑ+iδ)+2)−2(ϑ+iδ+1)2}p2(x)−4q(x)(ϑ+iδ+1)2|. |
Next, in order to find the bound on |a3|, by subtracting (2.10) from (2.8), we obtain
2(ϑ+iδ+2)a3−2(ϑ+iδ+2)a22=Lp,q,1(x)(u2−v2)+Lp,q,2(x)(u21−v21)2(ϑ+iδ+2)a3=Lp,q,1(x)(u2−v2)+2(ϑ+iδ+2)a22a3=Lp,q,1(x)(u2−v2)2(ϑ+iδ+2)+a22 | (2.15) |
Then, in view of (2.11) and (2.12), we have from (2.15)
a3=L2p,q,1(x)2(ϑ+iδ+2)2(u21+v21)+Lp,q,1(x)2(ϑ+iδ+2)(u2−v2). |
|a3|≤p2(x)|ϑ+iδ+1|2+p(x)|ϑ+iδ+2|=p2(x)(ϑ+1)2+δ2+p(x)√(ϑ+2)2+δ2 |
This completes the proof.
Taking δ=0, in Theorem 2.1, we get the following corollary.
Corollary 2.2. Let the function f(z) given by (1.1) be in the class B(ϑ). Then
|a2|≤p(x)√2p(x)√|{(ϑ2+3ϑ+2)−2(ϑ+1)2}p2(x)−4q(x)(ϑ+1)2| |
and
|a3|≤p2(x)(ϑ+2)2+p(x)ϑ+2 |
Also, taking ϑ=0 and δ=0, in Theorem 2.1, we get the results given in [18].
Fekete-Szegö inequality is one of the famous problems related to coefficients of univalent analytic functions. It was first given by [12], the classical Fekete-Szegö inequality for the coefficients of f∈S is
|a3−μa22|≤1+2exp(−2μ/(1−μ)) for μ∈[0,1). |
As μ→1−, we have the elementary inequality |a3−a22|≤1. Moreover, the coefficient functional
ςμ(f)=a3−μa22 |
on the normalized analytic functions f in the unit disk U plays an important role in function theory. The problem of maximizing the absolute value of the functional ςμ(f) is called the Fekete-Szegö problem.
In this section, we are ready to find the sharp bounds of Fekete-Szegö functional ςμ(f) defined for f∈B(ϑ,δ) given by (1.1).
Theorem 3.1. Let f given by (1.1) be in the class B(ϑ,δ) and μ∈R. Then
|a3−μa22|≤{p(x)√(ϑ+2)2+δ2, 0≤|h(μ)|≤12√(ϑ+2)2+δ22p(x)|h(μ)|, |h(μ)|≥12√(ϑ+2)2+δ2 |
where
h(μ)=L2p,q,1(x)(1−μ)((ϑ+iδ)2+3(ϑ+iδ)+2)L2p,q,1(x)−2Lp,q,2(x)(ϑ+iδ+1)2. |
Proof. From (2.14) and (2.15), we conclude that
a3−μa22=(1−μ)L3p,q,1(x)(u2+v2)[((ϑ+iδ)2+3(ϑ+iδ)+2)L2p,q,1(x)−2Lp,q,2(x)(ϑ+iδ+1)2]+Lp,q,1(x)2(ϑ+iδ+2)(u2−v2) |
=Lp,q,1(x)[(h(μ)+12(ϑ+iδ+2))u2+(h(μ)−12(ϑ+iδ+2))v2] |
where
h(μ)=L2p,q,1(x)(1−μ)((ϑ+iδ)2+3(ϑ+iδ)+2)L2p,q,1(x)−2Lp,q,2(x)(ϑ+iδ+1)2. |
Then, in view of (1.3), we obtain
|a3−μa22|≤{p(x)√(ϑ+2)2+δ2, 0≤|h(μ)|≤12√(ϑ+2)2+δ22p(x)|h(μ)|, |h(μ)|≥12√(ϑ+2)2+δ2 |
We end this section with some corollaries.
Taking μ=1 in Theorem 3.1, we get the following corollary.
Corollary 3.2. If f∈B(ϑ,δ), then
|a3−a22|≤p(x)√(ϑ+2)2+δ2. |
Taking δ=0 in Theorem 3.1, we get the following corollary.
Corollary 3.3. Let f given by (1.1) be in the class B(ϑ,0). Then
|a3−μa22|≤{p(x)ϑ+2, 0≤|h(μ)|≤12(ϑ+2)2p(x)|h(μ)|, |h(μ)|≥12(ϑ+2) |
Also, taking ϑ=0, δ=0 and μ=1 in Theorem 3.1, we get the following corollary.
Corollary 3.4. Let f given by (1.1) be in the class B. Then
|a3−a22|≤p(x)2. |
All authors declare no conflicts of interest in this paper.
[1] |
D. Fallis, What is disinformation?, Library Trends, 63 (2015), 401–426. https://doi.org/10.1353/lib.2015.0014 doi: 10.1353/lib.2015.0014
![]() |
[2] |
J. D. West, C. T. Bergstrom, Misinformation in and about science, Proc. Natl. Acad. Sci., 118 (2021), e1912444117. https://doi.org/10.1073/pnas.1912444117 doi: 10.1073/pnas.1912444117
![]() |
[3] |
T. Lin, M. Chang, C. Chang, Y. Chou, Government-sponsored disinformation and the severity of respiratory infection epidemics including COVID-19: A global analysis, 2001–2020. Soc. Sci. Med., 296 (2022), 114744. https://doi.org/10.1016/j.socscimed.2022.114744 doi: 10.1016/j.socscimed.2022.114744
![]() |
[4] | S. Bradshaw, P. N. Howard, The global organization of social media disinformation campaigns, J. Int. Aff., 71 (2018), 23–32. |
[5] |
A. Bessi, E. Ferrara, Social bots distort the 2016 US Presidential election online discussion, First Monday, 21 (2016). https://doi.org/10.5210/FM.V21I11.7090 doi: 10.5210/FM.V21I11.7090
![]() |
[6] |
T. R. Keller, U. Klinger, Social bots in election campaigns: Theoretical, empirical, and methodological implications, Political Commun., 36 (2019), 171–189. https://doi.org/10.1080/10584609.2018.1526238 doi: 10.1080/10584609.2018.1526238
![]() |
[7] |
E. Ferrara, O. Varol, C. Davis, F. Menczer, A. Flammini, The rise of social bots, Commun. ACM, 59 (2016), 96–104. https://doi.org/10.1145/2818717 doi: 10.1145/2818717
![]() |
[8] | N. J. Cull, V. Gatov, P. Pomerantsev, A. Applebaum, A. Shawcross, Soviet subversion, disinformation and propaganda: How the West fought against it, London LSE Consult., 68 (2017), 1–77. |
[9] |
Z. Li, Q. Zhang, X. Du, Y. Ma, S. Wang, Social media rumor refutation effectiveness: Evaluation, modelling and enhancement, Inform. Proc. Manage., 58 (2021), 102420. https://doi.org/10.1016/j.ipm.2020.102420 doi: 10.1016/j.ipm.2020.102420
![]() |
[10] | P. Ozturk, H. Li, Y. Sakamoto, Combating rumor spread on social media: The effectiveness of refutation and warning, in 2015 48th Hawaii international conference on system sciences, IEEE, (2015), 2406–2414. https://dx.doi.org/10.2139/ssrn.2564249 |
[11] |
G. Simons, D. Strovsky, Censorship in contemporary Russian journalism in the age of the war against terrorism: A historical perspective, Eur. J. Commun., 21 (2006), 189–211. https://doi.org/10.1177/0267323105064 doi: 10.1177/0267323105064
![]() |
[12] |
M. Eid, The new era of media and terrorism, Stud. Conflict Terrorism, 36 (2013), 609–615. https://doi.org/10.1080/1057610X.2013.793638 doi: 10.1080/1057610X.2013.793638
![]() |
[13] |
S. M. Alzanin, A. M. Azmi, Detecting rumors in social media: A survey, Proc. Comput. Sci., 142 (2018), 294–300. https://doi.org/10.1016/j.procs.2018.10.495 doi: 10.1016/j.procs.2018.10.495
![]() |
[14] |
F. Xu, V. S. Sheng, M. Wang, Near real-time topic-driven rumor detection in source microblogs, Knowl. Based Syst., 207 (2020), 106391. https://doi.org/10.1016/j.knosys.2020.106391 doi: 10.1016/j.knosys.2020.106391
![]() |
[15] | E. Alothali, N. Zaki, E. A. Mohamed, H. Alashwal, Detecting social bots on twitter: a literature review, in 2018 International conference on innovations in information technology (IIT), SAGA, (2018), 175–180. https://doi.org/10.1109/INNOVATIONS.2018.8605995 |
[16] |
N. Hajli, U. Saeed, M. Tajvidi, F. Shirazi, Social bots and the spread of disinformation in social media: the challenges of artificial intelligence, Br. J. Manage., 33 (2022), 1238–1253. https://doi.org/10.1111/1467-8551.12554 doi: 10.1111/1467-8551.12554
![]() |
[17] | C. Cai, L. Li, D. Zengi, Behavior enhanced deep bot detection in social media, in 2017 IEEE International Conference on Intelligence and Security Informatics (ISI), IEEE, (2017), 128–130. https://doi.org/10.1109/ISI.2017.8004887 |
[18] |
J. Li, H. Jiang, X. Mei, C. Hu, G. Zhang, Dynamical analysis of rumor spreading model in multi-lingual environment and heterogeneous complex networks, Inform. Sci., 536 (2020), 391–408. https://doi.org/10.1016/j.ins.2020.05.037 doi: 10.1016/j.ins.2020.05.037
![]() |
[19] |
J. Chen, C. Chen, Q. Song, Y. Zhao, L. Deng, R. Xie, et al., Spread mechanism and control strategies of rumor propagation model considering rumor refutation and information feedback in emergency management, Symmetry, 13 (2021), 1694. https://doi.org/10.3390/sym13091694 doi: 10.3390/sym13091694
![]() |
[20] |
L. Zhu, F. Yang, G. Guan, Z. Zhang, Modeling the dynamics of rumor diffusion over complex networks, Inform. Sci., 562 (2021), 240–258. https://doi.org/10.1016/j.ins.2020.12.071 doi: 10.1016/j.ins.2020.12.071
![]() |
[21] |
S. Yu, Z. Yu, H. Jiang, Stability, hopf bifurcation and optimal control of multilingual rumor-spreading model with isolation mechanism, Mathematics, 10 (2022), 4556. https://doi.org/10.3390/math10234556 doi: 10.3390/math10234556
![]() |
[22] |
T. Li, Y. Guo, Nonlinear dynamical analysis and optimal control strategies for a new rumor spreading model with comprehensive interventions, Qualitative theory of dynamical systems, 20 (2021), 1–24. https://doi.org/10.1007/s12346-021-00520-7 doi: 10.1007/s12346-021-00520-7
![]() |
[23] |
Z. Liu, T. Qin, Q. Sun, S. Li, H. H. Song, Z. Chen, SIRQU: Dynamic quarantine defense model for online rumor propagation control, IEEE Trans. Comput. Soc. Syst., 9 (2022), 1703–1714. https://doi.org/10.1109/TCSS.2022.3161252 doi: 10.1109/TCSS.2022.3161252
![]() |
[24] |
X. Wang, X. Wang, F. Hao, G. Min, L. Wang, Efficient coupling diffusion of positive and negative information in online social networks, IEEE Trans. Network Serv. Manage., 16 (2019), 1226–1239. https://doi.org/10.1109/TNSM.2019.2917512 doi: 10.1109/TNSM.2019.2917512
![]() |
[25] |
J. Zhao, L. Yang, X. Zhong, X. Yang, Y. Wu, Y. Y. Tang, Minimizing the impact of a rumor via isolation and conversion, Phys. A Stat. Mech. Appl., 526 (2019), 120867. https://doi.org/10.1016/j.physa.2019.04.103 doi: 10.1016/j.physa.2019.04.103
![]() |
[26] |
Y. Lin, X. Wang, F. Hao, Y. Jiang, Y. Wu, G. Min, et al., Dynamic control of fraud information spreading in mobile social networks, IEEE Trans. Syst. Man Cybernetics Syst., 51 (2019), 3725–3738. https://doi.org/10.1109/TSMC.2019.2930908 doi: 10.1109/TSMC.2019.2930908
![]() |
[27] |
Y. Cheng, L. Zhao, Dynamical behaviors and control measures of rumor-spreading model in consideration of the infected media and time delay, Inform. Sci., 564 (2021), 237–253. https://doi.org/10.1016/j.ins.2021.02.047 doi: 10.1016/j.ins.2021.02.047
![]() |
[28] |
J. B. Bak-Coleman, I. Kennedy, M. Wack, A. Beers, J. S. Schafer, E. S. Spiro, et al., Combining interventions to reduce the spread of viral misinformation, Nat. Hum. Behav., 6 (2022), 1372–1380. https://doi.org/10.1038/s41562-022-01388-6 doi: 10.1038/s41562-022-01388-6
![]() |
[29] |
Z. Zhao, Y. Liu, K. Wang, An analysis of rumor propagation based on propagation force, Phys. A Stat. Mech. Appl., 443 (2016), 263–271. https://doi.org/10.1016/j.physa.2015.09.060 doi: 10.1016/j.physa.2015.09.060
![]() |
[30] |
A. Yang, X. Huang, X. Cai, X. Zhu, L. Lu, ILSR rumor spreading model with degree in complex network, Phys. A Stat. Mech. Appl., 531 (2019), 121807. https://doi.org/10.1016/j.physa.2019.121807 doi: 10.1016/j.physa.2019.121807
![]() |
[31] |
Z. He, Z. Cai, J. Yu, X. Wang, Y. Sun, Y. Li, Cost-efficient strategies for restraining rumor spreading in mobile social networks, IEEE Trans. Veh. Technol., 66 (2016), 2789–2800. https://doi.org/10.1109/TVT.2016.2585591 doi: 10.1109/TVT.2016.2585591
![]() |
[32] |
L. Zino, M. Cao, Analysis, prediction, and control of epidemics: A survey from scalar to dynamic network models, IEEE Circuits Syst. Mag., 21 (2021), 4–23. https://doi.org/10.1109/MCAS.2021.3118100 doi: 10.1109/MCAS.2021.3118100
![]() |
[33] |
J. Chen, L. Yang, X. Yang, Y. Y. Tang, Cost-effective anti-rumor message-pushing schemes, Phys. A Stat. Mech. Appl., 540 (2020), 123085. https://doi.org/10.1016/j.physa.2019.123085 doi: 10.1016/j.physa.2019.123085
![]() |
[34] |
R. E. Kopp, Pontryagin maximum principle, Math. Sci. Eng., (1962), 255–279. https://doi.org/10.1016/S0076-5392(08)62095-0 doi: 10.1016/S0076-5392(08)62095-0
![]() |
[35] |
S. N. Ha, A nonlinear shooting method for two-point boundary value problems, Comput. Math. Appl., 42 (2001), 1411–1420. https://doi.org/10.1016/S0898-1221(01)00250-4 doi: 10.1016/S0898-1221(01)00250-4
![]() |
[36] | A. V. Rao, A survey of numerical methods for optimal control, Adv. Astronaut. Sci., 135 (2009), 497–528. |
[37] |
A. Bodaghi, J. Oliveira, The characteristics of rumor spreaders on Twitter: A quantitative analysis on real data, Comput. Commun., 160 (2020), 674–687. https://doi.org/10.1016/j.comcom.2020.07.017 doi: 10.1016/j.comcom.2020.07.017
![]() |
[38] |
Z. Yu, S. Lu, D. Wang, Z. Li, Modeling and analysis of rumor propagation in social networks, Inform. Sci., 580 (2021), 857–873. https://doi.org/10.1016/j.ins.2021.09.012 doi: 10.1016/j.ins.2021.09.012
![]() |
[39] |
M. Umer, Z. Imtiaz, S. Ullah, A. Mehmood, G. S. Choi, B. On, Fake news stance detection using deep learning architecture (CNN-LSTM), IEEE Access, 8 (2020), 156695–156706. https://doi.org/10.1109/ACCESS.2020.3019735 doi: 10.1109/ACCESS.2020.3019735
![]() |
[40] | M. Yglesias, This is the real truth about journalists' pay, Vox, 2015. |
[41] | Twitter Usage Statistics. Available from: https://www.internetlivestats.com/twitter-statistics/. |
[42] | S. Antoniadis, I. Litou, V. Kalogeraki, A model for identifying misinformation in online social networks, in On the Move to Meaningful Internet Systems: OTM 2015 Conferences: Confederated International Conferences, Springer, (2015), 473–482. https://doi.org/10.1007/978-3-319-26148-5_32 |
[43] | How Much Does a Cloud Server Cost for a Small Business. Available from: https://siriusofficesolutions.com/cloud-server-price/. |
[44] |
Y. Feng, J. Li, L. Jiao, X. Wu, Towards learning-based, content-agnostic detection of social bot traffic, IEEE Trans. Dependable Secure Comput., 18 (2020), 2149–2163. https://doi.org/10.1109/TDSC.2020.3047399 doi: 10.1109/TDSC.2020.3047399
![]() |
[45] |
D. Huang, L. Yang, P. Li, X. Yang, Y. Y. Tang, Developing cost-effective rumor-refuting strategy through game-theoretic approach, IEEE Syst. J., 15 (2020), 5034–5045. https://doi.org/10.1109/JSYST.2020.3020078 doi: 10.1109/JSYST.2020.3020078
![]() |
[46] |
D. Huang, L. Yang, X. Yang, Y. Y. Tang, J. Bi, Defending against online social network rumors through optimal control approach, Discrete Dyn. Nat. Soc., 2020 (2020), 1–13. https://doi.org/10.1155/2020/6263748 doi: 10.1155/2020/6263748
![]() |
[47] | S. Asur, B. A. Huberman, G. Szabo, C. Wang, Trends in social media: Persistence and decay, in Proceedings of the International AAAI Conference on Web and Social Media, (2011), 434–437. https://doi.org/10.1609/icwsm.v5i1.14167 |
1. | Ala Amourah, Basem Aref Frasin, Thabet Abdeljawad, Sivasubramanian Srikandan, Fekete-Szegö Inequality for Analytic and Biunivalent Functions Subordinate to Gegenbauer Polynomials, 2021, 2021, 2314-8888, 1, 10.1155/2021/5574673 | |
2. | Mohamed Illafe, Ala Amourah, Maisarah Haji Mohd, Coefficient Estimates and Fekete–Szegö Functional Inequalities for a Certain Subclass of Analytic and Bi-Univalent Functions, 2022, 11, 2075-1680, 147, 10.3390/axioms11040147 | |
3. | Nazmiye Yilmaz, İbrahim Aktaş, On some new subclasses of bi-univalent functions defined by generalized Bivariate Fibonacci polynomial, 2022, 33, 1012-9405, 10.1007/s13370-022-00993-y | |
4. | Daniel Breaz, Halit Orhan, Luminiţa-Ioana Cotîrlă, Hava Arıkan, A New Subclass of Bi-Univalent Functions Defined by a Certain Integral Operator, 2023, 12, 2075-1680, 172, 10.3390/axioms12020172 | |
5. | Luminiţa-Ioana Cotîrlǎ, Abbas Kareem Wanas, Applications of Laguerre Polynomials for Bazilevič and θ-Pseudo-Starlike Bi-Univalent Functions Associated with Sakaguchi-Type Functions, 2023, 15, 2073-8994, 406, 10.3390/sym15020406 | |
6. | Isra Al-Shbeil, Abbas Kareem Wanas, Afis Saliu, Adriana Cătaş, Applications of Beta Negative Binomial Distribution and Laguerre Polynomials on Ozaki Bi-Close-to-Convex Functions, 2022, 11, 2075-1680, 451, 10.3390/axioms11090451 | |
7. | Tariq Al-Hawary, Ala Amourah, Basem Aref Frasin, Fekete–Szegö inequality for bi-univalent functions by means of Horadam polynomials, 2021, 27, 1405-213X, 10.1007/s40590-021-00385-5 | |
8. | Abbas Kareem Wanas, Luminiţa-Ioana Cotîrlă, Applications of (M,N)-Lucas Polynomials on a Certain Family of Bi-Univalent Functions, 2022, 10, 2227-7390, 595, 10.3390/math10040595 | |
9. | Abbas Kareem Wanas, Haeder Younis Althoby, Fekete-Szegö Problem for Certain New Family of Bi-Univalent Functions, 2022, 2581-8147, 263, 10.34198/ejms.8222.263272 | |
10. | Arzu Akgül, F. Müge Sakar, A new characterization of (P, Q)-Lucas polynomial coefficients of the bi-univalent function class associated with q-analogue of Noor integral operator, 2022, 33, 1012-9405, 10.1007/s13370-022-01016-6 | |
11. | Tariq Al-Hawary, Coefficient bounds and Fekete–Szegö problem for qualitative subclass of bi-univalent functions, 2022, 33, 1012-9405, 10.1007/s13370-021-00934-1 | |
12. | Ala Amourah, Basem Aref Frasin, Tamer M. Seoudy, An Application of Miller–Ross-Type Poisson Distribution on Certain Subclasses of Bi-Univalent Functions Subordinate to Gegenbauer Polynomials, 2022, 10, 2227-7390, 2462, 10.3390/math10142462 | |
13. | Abbas Kareem Wanas, Alina Alb Lupaş, Applications of Laguerre Polynomials on a New Family of Bi-Prestarlike Functions, 2022, 14, 2073-8994, 645, 10.3390/sym14040645 | |
14. | Ibtisam Aldawish, Basem Frasin, Ala Amourah, Bell Distribution Series Defined on Subclasses of Bi-Univalent Functions That Are Subordinate to Horadam Polynomials, 2023, 12, 2075-1680, 362, 10.3390/axioms12040362 | |
15. | Ala Amourah, Omar Alnajar, Maslina Darus, Ala Shdouh, Osama Ogilat, Estimates for the Coefficients of Subclasses Defined by the Bell Distribution of Bi-Univalent Functions Subordinate to Gegenbauer Polynomials, 2023, 11, 2227-7390, 1799, 10.3390/math11081799 | |
16. | Omar Alnajar, Maslina Darus, 2024, 3150, 0094-243X, 020005, 10.1063/5.0228336 | |
17. | Muajebah Hidan, Abbas Kareem Wanas, Faiz Chaseb Khudher, Gangadharan Murugusundaramoorthy, Mohamed Abdalla, Coefficient bounds for certain families of bi-Bazilevič and bi-Ozaki-close-to-convex functions, 2024, 9, 2473-6988, 8134, 10.3934/math.2024395 | |
18. | Ala Amourah, Ibtisam Aldawish, Basem Aref Frasin, Tariq Al-Hawary, Applications of Shell-like Curves Connected with Fibonacci Numbers, 2023, 12, 2075-1680, 639, 10.3390/axioms12070639 | |
19. | Tariq Al-Hawary, Ala Amourah, Abdullah Alsoboh, Osama Ogilat, Irianto Harny, Maslina Darus, Applications of q−Ultraspherical polynomials to bi-univalent functions defined by q−Saigo's fractional integral operators, 2024, 9, 2473-6988, 17063, 10.3934/math.2024828 | |
20. | İbrahim Aktaş, Derya Hamarat, Generalized bivariate Fibonacci polynomial and two new subclasses of bi-univalent functions, 2023, 16, 1793-5571, 10.1142/S1793557123501474 | |
21. | Abbas Kareem Wanas, Fethiye Müge Sakar, Alina Alb Lupaş, Applications Laguerre Polynomials for Families of Bi-Univalent Functions Defined with (p,q)-Wanas Operator, 2023, 12, 2075-1680, 430, 10.3390/axioms12050430 | |
22. | Ala Amourah, Zabidin Salleh, B. A. Frasin, Muhammad Ghaffar Khan, Bakhtiar Ahmad, Subclasses of bi-univalent functions subordinate to gegenbauer polynomials, 2023, 34, 1012-9405, 10.1007/s13370-023-01082-4 | |
23. | Tariq Al-Hawary, Basem Aref Frasin, Abbas Kareem Wanas, Georgia Irina Oros, On Rabotnov fractional exponential function for bi-univalent subclasses, 2023, 16, 1793-5571, 10.1142/S1793557123502170 | |
24. | Tariq Al-Hawary, Ala Amourah, Hasan Almutairi, Basem Frasin, Coefficient Inequalities and Fekete–Szegö-Type Problems for Family of Bi-Univalent Functions, 2023, 15, 2073-8994, 1747, 10.3390/sym15091747 | |
25. | Omar Alnajar, Osama Ogilat, Ala Amourah, Maslina Darus, Maryam Salem Alatawi, The Miller-Ross Poisson distribution and its applications to certain classes of bi-univalent functions related to Horadam polynomials, 2024, 10, 24058440, e28302, 10.1016/j.heliyon.2024.e28302 | |
26. | Tariq Al-Hawary, Basem Frasin, Daniel Breaz, Luminita-Ioana Cotîrlă, Inclusive Subclasses of Bi-Univalent Functions Defined by Error Functions Subordinate to Horadam Polynomials, 2025, 17, 2073-8994, 211, 10.3390/sym17020211 |