VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media
Authors: Yizhou Zhang
Karishma Sharma
Yan Liu
Venue: NeurIPS 2021 Poster (2021)
Keywords: Coordinated Influence Campaigns
Disinformation
Social Media
Fake News
Temporal Point Process
Variational Inference

Abstract: Recent years have witnessed an increasing use of coordinated accounts on social media, operated by misinformation campaigns to influence public opinion and manipulate social outcomes. Consequently, there is an urgent need to develop an effective methodology for coordinated group detection to combat the misinformation on social media. However, existing works suffer from various drawbacks, such as, either limited performance due to extreme reliance on predefined signatures of coordination, or instead an inability to address the natural sparsity of account activities on social media with useful prior domain knowledge. Therefore, in this paper, we propose a coordination detection framework incorporating neural temporal point process with prior knowledge such as temporal logic or pre-defined filtering functions. Specifically, when modeling the observed data from social media with neural temporal point process, we jointly learn a Gibbs-like distribution of group assignment based on how consistent an assignment is to (1) the account embedding space and (2) the prior knowledge. To address the challenge that the distribution is hard to be efficiently computed and sampled from, we design a theoretically guaranteed variational inference approach to learn a mean-field approximation for it. Experimental results on a real-world dataset show the effectiveness of our proposed method compared to the SOTA model in both unsupervised and semi-supervised settings. We further apply our model on a COVID-19 Vaccine Tweets dataset. The detection result suggests the presence of suspicious coordinated efforts on spreading misinformation about COVID-19 vaccines.

VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media
Yizhou Zhang', Karishma Sharma", Yan Liu Department of Computer Science
Viterbi School of Engineering University of Southern California {zhangyiz, krsharma, yanl iu. cs @usc..e
Abstract
Recent years have witnessed increasing use of coordinated accounts on social media, operated by misinformation campaigns to influence public opinion and manipulate social outcomes. Consequently, there an urgent need develop an effective methodology for coordinated group detection to combat the misinforma- tion on social media. However, the sparsity of account activities on social media limits the performance of existing deep learning based coordination detectors they can not exploit useful prior knowledge. Instead, the detectors incorporated with prior knowledge suffer from limited expressive power and poor performance. Therefore, in this paper we propose a coordination detection framework incor- porating neural temporal point process with prior knowledge such as temporal logic or pre-defined filtering functions. Specifically, when modeling observed data from social media with neural temporal point process. we jointly learn Gibbs distribution of group assignment based on how consistent an assignment is to (1) the account embedding space and (2) the prior knowledge. To address the challenge that the distribution is hard to be efficiently computed and sampled from, design theoretically guaranteed variational inference approach learn a mean-field approximation for it. Experimental results on a real-world dataset show the effectiveness of our proposed method compared to state-of-the of-the-rrt model in both unsupervised and semi-supervised settings. We further apply our model COVID-19 Vaccine Tweets dataset. The detection result suggests presence of suspicious coordinated efforts on spreading misinformation about COVID-19 vaccines.
1 Introduction
Recent research reveals that the information diffusion on social media is heavily influenced by hidden account groups [1] 30) 31), many of which are coordinated accounts operated by misinformation campaigns (an example shown in Fig. Tap. This form of abuse to spread misinformation has been seen in different fields, including politics (e.g. the election) [20] and healthcare (e.g. the ongoing COVID- 19 pandemic) [31]. This persistent abuse as well as the urgency to combat misinformation prompt us to develop effective methodologies to uncover hidden coordinated groups from the diffusion cascade of information on social media.
On social media, the diffusion cascade of a piece of information (like a tweet) can be considered as realization of marked temporal point process where each mark of an event type corresponds to account. Therefore, we can formulate uncovering coordinated accounts as detecting mark groups from observed point process data, which leads to natural solution that first acquires account
'Equally contributed
35th Conference on Neural Information Processing Systems (NcurIPS 2021).

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(a) Example of collaborated hchaviours.
(b) Frequency statistic of accounts.
The figure (a) is an example of coordinated accounts detected by our method on Twitter. They retweet similar anti-vaccine contents about COVID-19 Vaccines from same or different sources. The figure (b) is the frequency statistic of accounts in IRA dataset about the U.S. 2016 Election.
Figure 1:
embeddings from the observed data with deep learning (e.g. neural temporal point process) and then conducts group detection in the embedding space 20 32. However, the data from social media has a special and important property, which is that the appearance of accounts in the diffusion cascades usually follows a long-tail distribution [8]] (an example shown in Fig. lb). This property brings a unique challenge: compared to few dominant accounts, most accounts appear sparsely in the data, limiting the performance of deep representation learning based models. Some previous works exploiting pre-defined collective behaviours [2 37 [2]] can circumvent this challenge. They mainly follow the paradigm that first constructs similarity graphs from the data with some prior knowledge or hypothesis and then conducts graph based clustering. Their expressive power, however, is heavily limited as the complicated interactions are simply represented as edges with scalar weights, and they exhibit strong reliance on predefined signatures of coordination. As a result, their performances are significantly weaker than the state-of-the-an deep representation learning based model (32
To address above challenges, we propose knowledge informed neural temporal point process model, named Variational Inference for Group Detection (VigDet). represents the domain knowledge of collective behaviors of coordinated accounts by defining different signatures of coordination. such as accounts that co-appear, or are synchronized in time, are more likely to be coordinated. Different from previous works that highly rely on assumed prior knowledge and cannot effectively learn from the data 2 VigDet encodes prior knowledge as temporal logic and power functions so that guides the learning of neural point process model and effectively infer coordinated behaviors. In addition, it maintains a distribution over group assignments and defines a potential score function that measures the consistency of group assignments in terms of both embedding space and prior knowledge, As a result, VigDet can make effective inferences over the constructed prior knowledge graph while jointly learning the account embeddings using neural point process.
crucial challenge in our framework is that the group assignment distribution, which is a Gibbs distribution defined on a Conditional Random Field [17], contains partition function as normalizer 16] Consequently it is NP-hard to compute or sample, leading to difficulties in both learning and inference 4 15) To address this issue, we apply variational inference 22 Specifically, we approximate the Gibbs distribution as a mean field distribution (24.. Then we jointly learn the approximation and learnable parameters with EM algorithm to maximize the evidence lower bound (ELBO) [22 of the observed data likelihood. In the E-step, we freeze the learnable parameters and infer the optimal approximation, while in the M-step, we freeze the approximation and update the parameters to maximize an objective function which is lower bound of the ELBO with theoretical guarantee. Our experiments on a real world dataset (20) involving coordination detection validate the effectiveness of our model compared with other baseline models including the current state of the art. We further apply our method on dataset of tweets about COVID-19 19 vaccine without ground-truth coordinated group label. The analysis on the detection result suggests the existence of suspicious coordinated efforts to spread misinformation and conspiracies about COVID-19 vaccines.
2
Related Work
2.1 Graph based coordinated group detection
One typical coordinated group detection paradigm is to construct graph measuring the similarity or interaction between accounts and then conduct clustering the graph or the embedding acquired

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by factorizing the adjacency matrix. There are two typical ways to construct the graph. One way is to measure the similarity or interaction with pre-defined features supported by prior knowledge or assumed signatures of coordinated or collective behaviors, such as co-activity, account clickstream and time sychronization [5] 29 37]. The other way to learn an interaction graph by fitting the data with the temporal point process models considering mutually influence between accounts as scalar scores as in traditional Hawkes Process [41]. A critical drawback of both methods is that the interaction between two accounts is simply represented as edge with scalar weight, resulting in poor ability to capture complicated interactions. based methods unsatisfactory due to reliance on the quality of prior knowledge or hypothesis of collective behaviors, which may vary with time
addition, the performances of prior knowledge
2.2 Representation learning based coordinated group detection
To address the reliance to the quality of prior knowledge and the limited expressive power of graph based method, recent research tries to directly learn account representations from the observed data. In (20). Inverse Reinforcement Learning (IRL) is applied to learn the reward behind an account's 's observed behavior and the learnt reward is forwarded into a classifier as features. However, since different accounts' activity traces modeled independently, it is hard for IRL to model the interactions among different accounts. The current state-of state-of-the-ar method in this direction is a neural temporal point process model named AMDN-HAGE 32] Its backbone (AMDN), which can efficiently capture account interactions from observed activity traces, contains an account embedding layer, history encoder and event decoder. The account embedding vectors optimized under the regularization of a Gaussian Mixture Model (the HAGE part). However, as a data driven deep learning model, the learning process of AMDN--AGE lacks the guidance of prior knowledge from human. In contrast, we propose VigDet, framework integrating neural temporal point process together and prior knowledge address inherent sparsity of account activities.
3 Preliminary and Task Definition
3.1 Marked Temporal Point Process
marked temporal point process (MTPP) is stochastic process whose realization discrete event sequence S [(v1, 4, (v2, (2), (13, (z).... (Un, [n)] where E V is the type mark of event i and 4 E +R+ is the timestamp [8. We denote the historical event collection before time / as H. {(v,, 4:)44 < 1). Given a history Hr, the conditional probability that an event with mark Ht) exp (-Sr_, du (s( where X, (4) HH)), also known as intensity function, is defined as (t = ElaN. (1) i.e. the derivative dt of the total number of events with type mark happening before or at time t, denoted as N, In social media data, Hawkes Process (HP) [41] is the commonly applied type of temporal point process. In Hawkes Process, the intensity function is defined as (t(HH,) = M. E E(e,,AeH au,s, A(t - t,) where Me 0 is the self activating intensity and av,,, 0 is the mutually triggering intensity modeling mark U;'s 's influence on v and k is a decay kernel to model influence decay over time.
E V happens at time is formulated as: (t
3.2 Neural Temporal Point Process
In Hawkes Process, only the µ and a are learnable parameters. Such weak expressive power hinders Hawkes Process from modeling complicated interactions between events. Consequently, researchers conduct meaningful trials modeling the intensity function with neural networks 21] 40) 44 33 23 32). In above works, the most recent work related to coordinated group detection is AMDN HAGE (32), whose backbone architecture AMDN is neural temporal point process model that encodes an event sequence S with masked self-attention:
A = =(KK /Va), == F(AV), Q XW,, K XW V = XW,
where o is masked activation function avoiding encoding future events into historical vectors, X IR_x Lxd (L is the sequence length and d the feature dimension) is the event sequence feature, F is a feedforward neural network or a RNN that summarizes historical representation from the attentive layer into context vectors C e 2xxd' and W WR. W, are learnable weights. Each row
3

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X; in X (the feature of event (i, t+)) is concatenation of learnable mark (each mark corresponds to an account on social media) embedding E.,, position embedding pos== with trigonometric integral function [35 and temporal embedding o01; 1;-1) using translation-invarian temporal
kernel function 38 After acquiring C, the likelihood of sequence S given mark embeddings E
and other parameters in AMDN, denoted as 02. can be modeled as:
log (S|E) [ogg((s(C() + logp(t((Ci))
K p(u;(C;) softmax(MLP(C))); p(4|C:) = Σ 2 k=1
(log t4 - m1) 2(
(2)
exp
W; = OVWC + bw), 8 cxpxxx b,), μi = V2Ci + bb
3.3 Task Definition: Coordinated Group Detection on Social Media
In coordinated group detection, we are given a temporal sequence dataset S -S, Sip from social media, where each sequence S = ((051,41) (U/2, 422), corresponds to a piece of information, e.g. tweet, and each event (vaj, 4iz) means that an account vij V (corresponding to type mark in MTPP) interacts with the tweet (like comment or retweet) at time 4aj. Supposing that the V consists of M groups, V. B/ E B.....MM,, This task can be conducted under unsupervised or semi supervipee setting. In unsupervised setting, do not have the group identity of any account. As for the semi-supervisee setting, the ground-truth group identity Yz of small account fraction V2 C V is accessible. Current sfate-of-the-art model on this task is AMDN-HAGE with k-Means. It first learns the account embeddings E with AMDN-HAGE. Then it obtains group assignment using k-Means clustering on learned E.
objective is to learn group assignment Y {yolv
Proposed Method: VigDet
In this section, we introduce our proposed model called VigDet (Variational Inference for Group Detection), which bridges neural temporal point process and graph based method based prior knowledge. Unlike the existing methods, in VigDet we regularize the learning process of the account embeddings with the prior knowledge based graph so that the performance can be improved. Such method addresses the heavy reliance of deep learning model on the quality and quantity of data as well as the poor expressive power of existing graph based methods exploiting prior knowledge.
4.1 Prior Knowledge based Graph Construction
For the prior knowledge based graph construction, we apply co-activity (29 to measure the similarity of accounts. This method assumes that the accounts that always appear together in same sequences are more likely to be in the same group. Specifically, we construct a dense graph G =< V,E E whose node set is the account set and the weight Wae of an edge (u, v) is the co-occurrence:
Wwe Σ1 EI(MEE)) (S)
SES
However, when integrated with our model, this edge weight is problematic because the coordinated accounts may also appear in the tweets attracting normal accounts. Although the co-occurrence of coordinated account pairs is statistically higher than other account pairs, since coordinated accounts are only small fraction of the whole account set, our model will tend more to predict an account as normal account. Therefore, we apply one of following two strategies to acquire filtered weight whe
Power Function based filtering: the co-occurrence of coordinated account pair statistically higher than a coordinated-norman pairs. Thus, we can use a power function with exponent > (p is hyper-parameter) to enlarge the difference and then conduct normalization:
W = Σ I((u E S) (v E S))
SES

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M-step: Update Embedding and Potential Function with SGD to Fit the Knowledge Informed Prediction and the Observed Data
Observed Data
Data-Driven Model
max Tog F)
Modeling the Embedding-bused Polential Function via a Neural Network
Knowledge
Q00
II Qu(yn) wEV
Informed Prediction
Prior
Graph Construction
man Px (P)
Knowledge
E-step: Enhance Prediction with
Belief Propagation Prior
Knowledge based Graph
Figure 2: The overview of VigDet. In this framework, we aim at learning knowledge informed data-driven model. To this end, based on prior knowledge we construct a graph describing the potential of account pairs to be coordinated. Then we alternately enhance the prediction of the data-driven model with the prior knowledge based graph and further update the model to fit the enhanced prediction well the observed data.
where u S and S mean that and appear in the sequence respectively. Then the weight with relatively low value will be filtered via normalization (details in next subsection).
Temporal Logic [19] based filtering: We can represent some prior knowledge a logic expression of temporal relations, denoted as r(-). and then only count those samples satisfying the logic expressions. Here, assume that the active time of accounts of the same group more likely to be similar. Therefore, we only consider the account pairs whose active time overlap is larger than a threshold (we apply half day, i.e. 12 hours):
w'. = Σ I((u ((uES)) (UEES)) S),r
SES
(5)
U, (min Ext max(tus, tus) > c)
where fut, tut are the last time that u and appears in the sequence and bus, bus are the first (starting) time that and appears in the sequence.
4.2 Integrate Prior Knowledge and Neural Temporal Point Process
To integrate prior knowledge and neural temporal point process, while maximizing the likelihood of the observed sequences p(S) E) given account embeddings, VigDet simultancously learns a distribution over group assignments defined by the following potential score function given the account embeddings E. and the prior knowledge based graph G =<VEE>
(Y; E,
Σ Po E.,))
Σ (0.0)EE
@o Vor u)
where sp (ya, E,,) is a learnable function measuring how an account's group identity yn. is consistent to the learnt embedding, e.g. feedforward neural network. And de (Yus Bos u, pre-defined
(g Mos u. v) Vd
Wur
1 yo)
where du. d, Ex Wuke Ex Wor are the degrees of and (y yv) is indicator function that equals when input is true and 0 otherwise. By encouraging co-appearing accounts to be assigned in to the same group, ( No; u, regularizes E and pp with prior knowledge. With the above potential score function, we can define the conditional distribution of group assignment Y given embedding E and the graph G:
P(Y|E, exp((((:E.9))
(8)
5

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where --E, exp( E, ()) is the normalizer keeping P(Y|E. .) distribution, also known as partition function [T 14. sums up exp( 9)) for all possible assignment Y. As a result, calculating P(Y|E, g) accurately and finding the assignment maximizing +(Y; g) are both NP-hard 4 [5]. Consequently, we approximate P(Y 9) with mean field distribution Q(Y) eey (v..). To inform the learning of F and Se with the prior knowledge behind g we propose to jointly learn Q, E' and pe by maximizing following objective function, which the Evidence Lower Bound (ELBO) of the observed data likelihood log (E) given embedding E:
O F,, sPo; S, G) = logg(S)F) PxI(Q112)
In this objective function, the first term is the likelihood of the obeserved data given account embeddings, which can be modeled as Eses log Pa. (SIE) with neural temporal point process model like AMDN. The second term regularizes the model to learn E and ips such that P(Y)E, G) can be approximated by its mean field approximation as precisely as possible. Intuitively, this be achieved when the two terms in the potential score function, i.e. Σuev (o Eu)) and Cn..EE (1h Mee U, v) agree with each other every possible Y. The above lower bound can be
optimized via variational EM algorithm [22 27 28 34
4.2.1 E-step: Inference Procedure.
In E-step, we aim at inferring the optimal Q(Y) that minimizes Dxz (QIP). Note that the formula- tion of (Y;; E, G) is same as Conditional Random Fields (CRF) 17 model although their learnable parameters are different. In E-step such difference is not important as all parameters in (YY; E, g)
are frozen. As existing works about CRF [16 14] have theoretically proven, following iterative updating function of belief propagation converges at local optimal solution
Qu(Ua = m) = (ya m)
exp
E. Σ DEV IIM'S'S
Óg(m, m', u, u) = m')}
Zu
Z
(10)
where Qu(y m) is the probability that account is assigned into group m and Zu
Σ Eismem Qu(y = m) is the normalizer keeping Qu valid distribution.
4.2.2 M-step: Learning Procedure.
In M-step, given fixed inference of Q we aim at maximizing Om:
OM (S)p((E)) (KI(Q]IP) (())( + Ey~q log P(Y|E,9) + const
(1I)
The key challenge M-step that calculating Ey~q log P(Y 9) is NP-hard 4 L5). To address
this challenge, we propose to alternatively optimize following theoretically justified lower bound:
Theorem 1. Given a fixed inference of Q and a pre-defined Φg. we have following inequality:
expl<polyu En)} Eiem'EM explyoomm' E.}} + const
Ey~q log P(Y|E,)) Ey-q Σ log
WEV
explypa(m, Ex)}
Σ Σ REVISMSM
Qulu =m)log
+ const
Eismes explspomm) (m'' Ex)}
(12)
The proof of this theorem is provided in the Appendix. Intuitively, the above objective function treats the Q as a group assignment enhanced label propagation on the prior knowledge based graph and encourages E and (2 to correct themselves by fitting the enhanced prediction. Compared with pseudolikelihood (3 which is applied to address similar challenges recent works [27], the proposed lower bound has a computable closed-form solution. Thus, we do not really need to sample Y from Q so that the noise is reduced. Also, this lower bound does not contain de explicitly in the non-constant term. Therefore, we can encourage the model to encode graph information into the embedding.
? A detailed justification is provided in the appendix

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4.2.3
Joint Training:
The E-step and M-step form closed loop. To create starting point, we initialize E' with the embedding layer of a pre-trained neural temporal process model (in this paper we apply AMDN- HAGE) and initialize yP via clustering learnt E (like fitting the Pa the prediction of k-Means). After that we repeat E-step and M-step to optimize the model. The pseudo code of the training algorithm is presented in Alg.
Algorithm Training Algorithm of VigDet.
Require: Dataset & and pre-defined G and Φg
Ensure: Well trained Q. E and spe
1: Initialize E with the embedding layer of AMDN-HAGE pre-trained on S.
2: Initialize Po on the initialized E.
3: while not converged do
4: Acquire Q by repeating Eq. 10 with E, yo and @g until convergence. (E-sstpp
5: Po; + argmax E..E (S + Ey-q Enev log exp
LisH'sM exp(vp((m' (M-step)
6: end while
4.3 Semi-supervised extension
The above framework does not make use of the ground-truth label the training procedure. In semi-super setting, we actually have the group identity Yz of small account fraction V/2 C V. Under this setting, we can naturally extend the framework via following modification to Alg. [ For account VL. set Qu one-hot distribution, where Qu = v/,) 1 for the groundtruth identity yr, and Qu (yv = m) = for other m E M,,
Experiments
5.1 Coordination Detection on IRA Dataset
utilize Twitter dataset containing coordinated accounts from Russia's Internet Research Agency (IRA dataset 20 32) attempting to manipulate the U.S. 2016 Election. The dataset contains tweet sequences (i.e., tweet with account interactions like comments, replies or retweets) constructed from the tweets related to the U.S. 2016 Election.
This dataset contains activities involving 2025 Twitter accounts, Among the 2025 accounts, 312 identified through U.S. Congress investigations as coordinated accounts and other 1713 accounts are normal accounts joining in discussion about the Election during the period of activity of those coordinated accounts. This dataset is applied for evaluation of coordination detection models in recent works (20 32]. In this paper. we apply two settings: unsupervised setting and superviee setting. For unsupervised setting, the model does not use any ground-truth account labels in training (but for hyperparameter selection, we hold out 100 randomly sampled accounts as validation set, and evaluate with reported metrics on the remaining 1925 accounts test set). For the semi semi- cemi-supervised setting, we similarly hold out 100 accounts for hyperparameter selection as validation set, and another 100 accounts with labels revealed in training set for semi-supervised training). The evaluation is reported on the remaining test set of 1825 accounts. The hyper parameters of the backbone of VigDet (AMDN) follow the original paper (32). Other implementation details are in the Appendix.
5.1.1 Evaluation Metrics and Baselines
In this experiment, we mainly evaluate the performance of two version of VigDet: VigDet (PF) and VigDet (TL). VigDet (PF) applies Power Function based filtering and VigDet (TL) applies Temporal Logic based filtering. For the in VigDet (PF), we apply 3. We compare them against existing approaches that utilize account activities identify coordinated accounts.
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7

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Table 1: Results on unsupervised coordination detection (IRA) on Twitter 2016 U.S. Election
Method (Unsupervised)
AP
AUC
F1
Prec
Rec
MaxFl
MacroF1
Co-activity
16.9
52.5
24.6
17.8
40.7
27.1
49.5
Clickstream
16.5
53.2
21.0
20.6
21.6
21.0
53.1
IRL
23.9
68.7
35.3
27.5
49.4
38.6
58.8
HP
29.8
56.7
44.2
42.1
46.6
46.0
66.7
AMDN-HAGE
80.5
89.9
69.6
94.3
55.5
75.8
82.7
AMDN-HAGE + k-Means
82.0
93.3
73.0
90.9
61.2
77.0
84.5
VigDet-PL(TLL)
83.3
94.0
70.7
89.6
59.0
77.8
83.2
VigDet-E(TL))
85.5
94.6
73.1
95.3
59.4
79.5
84.6
VigDet(TL)
86.1
94.6
73.4
95.1
59.9
79.6
84.8
VigDet-PL(PF)
84.5
95.0
71.9
91.4
59.6
79.3
83.9
VigDet-E(PF)
85.1
94.3
73.6
92.7
61.2
78.8
84.9
VigDet(PF)
87.2
95.0
75.2
91.7
63.9
79.3
85.7
Unsupervised Baselines: Co-activity clustering
29 and Clickstream clustering [37 are based
pre-defined similarity graphs. HP (Hawkes Process) 41 is a learnt graph based method. [RI
and AMDN-HAGE 32] are two recent representation learning method.
Semi-Supervised Baselines: Semi-NN semi-supervised feedforward neural network without requiring additional graph structure information. It is trained with self-training algorithm 43 26 Label Propagation Algorithm (LPA)
42 and Graph Neural Network (GNN) (we use the GCN 13
the most representative GNN) B
two baselines incorporated with graph structure. In LPA and GNN, for the graph structures (edge features), we use the PF and TL based prior knowledge graphs (similarly used in VigDet), well the graph learned HP model edge features. For the node features in GNN, we provide the account embeddings learned with AMDN-HAGE.
36 I0]
Ablation Variants: To verify the importance of the EM-based variational inference framework and our proposed objective function in M-step, we compare our models with two variants: VigDet-E and VigDet-PL (PL for Pseudo Likelihood). In VigDet-E, assignments (inferred distribution over labels) enhanced with prior knowledge, but without alternating updates using the EM loop. It is similar some existing works conducting post-processig with CRF to enhance prediction based on the learnt representations 12). In VigDet-PL, we replace our proposed objective function with pseudo likelihood function from existing works.
only conduct E-step once to acquire group
Metries: We compare two kinds of metrics. One kind threshold-free: Average Precision (AP), area under the ROC curve (AUC), and maxF1 at threshold that maximizes Fl score. The other kind need threshold: F1, Precision, Recall, and MacroF1. For this kind, we apply 0.5 as threshold for the binary (coordinated/normall account) labels..
5.1.2 Results
Table I and 2 provide results of model evaluation against the baselines averaged in the IRA dataset over five random seeds. As we can see, VigDet, as well as its variants, outperforms other methods on both unsupervised and semi-supervised settings, due to their ability to integrate neural temporal point process, which is the current state-of-the-art method, and prior knowledges, which are robust to data quality and quantity. It is noticeable that although GNN based methods can also integrate prior knowledge based graphs and representation learning from state-of of-the-art model, our model still outperforms it by modeling and inferring the distribution over group assignments jointly guided by consistency in the embedding and prior knowledge space.
Besides baselines, we also compare VigDet with its variants VigDet-E and VigDet-PL. As we can see, for Power Filtering strategy, compared with VigDet-E, VigDet achieves significantly better result on most of the metrics in both settings, indicating that leveraging the EM loop and proposed M-step optimization can guide the model to learn better representations for E and so. As for Temporal Logic Filtering strategy, VigDet also brings boosts, although relatively marginal. Such phenomenon suggests that the performance our M-step objective function may vary with the prior knowledge applied. Meanwhile, the VigDet-PL performs not only worse than Vig but also
Ablation Test:

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Table 2: Results on semi-supervised coordination detection (IRA) on Twitter 2016 U.S. Election
Method (Semi-Supervins
AP
AUC
F1
Prec
Rec
MaxFI
MacroF1
LPA(HP)
63.3
76.8
68.1
76.2
61.8
71.6
81.5
LPA
69.7
85.9
62.3
88.5
48.6
66.1
78.6
LPA(PF)
71.1
85.3
60.8
66.5
56.4
68.3
77.2
AMDN-HAGE Semi-NN
77.1
87.8
70.5
76.6
65.5
72.3
82.8
AMIN-HAGE GNN (HP)
75.5
84.0
72.0
83.0
65.1
76.6
83.7
AMIN-HAGEE GNN (PF)
80.6
89.5
73.0
86.3
63.7
76.4
84.5
AMDN-HAGE GNN (TL)
81.3
90.2
73.6
78.2
70.2
77.2
84.6
VigDet-PL(TL)
87.7
95.5
73.9
94.2
61.4
80.0
85.1
VigDet-E(TL)
88.1
95.7
73.4
94.6
60.4
80.8
84.8
VigDet(TL)
88.0
95.7
73.6
94.2
60.9
80.8
84.9
VigDet-PL(PF)
85.1
95.3
69.7
93.4
55.9
79.0
82.8
VigDet-E(PF)
87.1
95.2
74.4
92.8
62.4
79.7
85.3
VigDet(PF)
87.6
95.6
76.1
87.2
68.1
79.8
86.2
worse than VigDet-E. This phenomenon shows that the pseudolikelihood noisy for VigDet and verifies the importance of our objective function.
5.2 Analysis on COVID-19 Vaccines Twitter Data
We collect tweets related COVID-19 Vaccines using Twitter public API, which provides 1% random sample of Tweets.
covid19
-ovidLO
vaccine
"V9ccine
The dataset contains 62k activity sequences of 31k accounts, after filtering accounts collected less than times in the collected tweets, and sequences shorter than length 10. Although the data of tweets about COVID-19 Vaccine does have ground- truth labels, we can apply VigDet to detect suspicious groups and then analyze the col- lective behavior of the group. The results bolster our method by mirroring observa- tions in other existing researches [11
-cononaviuu
covidvaccine
-covid
carenaviuus
-staysafe
onpoli
-maskup
-smallbusiness
trudeauvaccinefall
redbubbbl
trudeaafflledddaaddd
covéd
Coordirated grrupp
piizer-
-VDccii
trudeaumustgo
Normel group
canada
hheltthcare
vaccination
maahhnnleamin
astrazeneca
artifidiaiiteelignnee
lockdoow
vasccination
scamdemic2020
-cdnip
covid:
-covid15vacciee
cdnmedia
largestva.cinedrre
crimesagainthuumanty
wearra weaarmask
masksdo ntwork
health
netocaronavinsswenn www.iiinivavainneeeee
tr trump
VigDet detects 8k suspicious accounts from the 31k Twitter accounts. We inspect tweets and account features of the detected suspicious group of coordi- nated accounts.
Detection:
trudeau
christmas
plandemic2020
-nddlatighhsccoomn
Ivingnotladkdoww
lockdownchaos
traahdeanatr
speratinnrraathe
Figure 3: Top-30 hashtags in tweets of suspicious coor- dinated group and normal group.
Representative tweets: We use topic min- ing tweets of detected coordinated ac- counts and show the text contents of the top representative tweets in Table 3
Account features: The two groups (detected coordinated and normal accounts) are clearly dis- tinguished in the comparison of top-30 hashtags in tweets posted by the accounts in each group (presented in Fig. 33. bold are the non-overlapping hashtags. The coordinated accounts seem to promote that the pandemic is hoax (#scamdemicc0020, #plandemic2020), as well as anti-mask. anti-vaccine and anti-lockdown (fhotcoronavinusvaccines #masksdontwork,, #livingnotlockdown) narratives, and political agendas (#trudeaumustgo) The normal accounts narratives are more general and show more positive attitudes towards vaccine, mask and prevention protocols.
Also, we measure percentage of unreliable and conspiracy news sources shared in the tweets of the detected coordinated accounts, which 55.4%, compared 23.2% the normal account group. The percentage of recent accounts (created in 2020-21) 21) is higher in coordinated group (20.4%) compared

---

Table 3: Representative tweets from topic clusters in tweets of detected suspicious coordinated
accounts,
If mRNA vaccines can cause autoimmune problems and more severe reactions to coronavirus' maybe that's why Gates is so confident he's onto winner when he predicts a more lethal pandemic coming down the track. The common cold could now kill millions but it will be called CV21/22?
This EXPERIMENTAL' "rushed science" gene therapy INJECTION of an UNKNOWN substance (called "vaccine" JUST TO AVOID LITIGATION of UNKNOWN SIDE EFFECTS) has skipped all regular animal testing and is being forced into LIVE HUMAN TRIAL.. it seems to he little henefit us really!
This Pfizer doesn't 't stop transmission. So why are they pushing it when preveet infection or kill the virus, merely reduces symptoms. Aoolation/Lockdowee /masks will still be required. Rather sinister especially when the complction date for trials, was/is 2023
is - You doní own anything, including your body. Full and absolute ownership of your biological being. - Disruption of your immunc system. Maximizing gains for #BillGatesBio Terrorist. # #Transhumanism #Dehumanization"
It is embarrassing to see Sturgoon fawning all over them. The rollout of the vaccine up here is agonisingly slow and I wouldn't 't be surprised if she was trying to show solidarity with the EU. There are more benefits being part the UK than the EU.
It also may be time for that "boring" O'Toole (as you label him) get little louder and tougher. To speak up more. To contradict Trudeau on this vaccine rollout and supply mess, O'Toole has no "fire". He can't do "blood sport". He's sidelined by far right diversions.
to 15.3% otherwise. Disinformation and suspensions are not exclusive to coordinated activities, and suspensions are based on Twitter manual process and get continually updated over time, also accounts created earlier can include recently compromised accounts; therefore, these measures cannot be considered as absolute ground-truth.
Conclusion
In this work, we proposed a prior knowledge guided neural temporal point process to detect coordi- nated groups on social media. Through a theoretically guaranteed variational inference framework, it integrate data-driven neural coordination detector with prior knowledge encoded as a graph. Comparison experiments and ablation test on IRA dataset verify the effectiveness of our model and inference. Furthermore, we apply our model to uncover suspicious misinformation campaign in COVID-19 vaccine related tweet dataset. Behaviour analysis of the detected coordinated group suggests efforts to promote anti-vaccine misinformation and conspiracies on Twitter.
However, there are still drawbacks of the proposed work. First, the current framework can only support one prior knowledge based graph as input. Consequently, if there are multiple kinds of prior knowledge, we have to manually define integration methods and parameters like weight. If an automatic integration module can be proposed, we expect that the performance of VigDet can be further improved. Secondly, as a statistical learning model, although integrated with prior knowledge, VigDet may have wrong predictions, such as mislabeling normal accounts as coordinated or missing some true coordinated accounts. Therefore, we insist that VigDet should be only considered as an efficient and effective assistant tool for human verifiers or researchers to accelerate filtering of suspicious accounts for further investigation or analysis. However, the results of VigDet, including but not limited to the final output scores and the intermediate results, should not be considered as any basis or evidence for any decision, judgement or announcement.
Acknowledgments and Disclosure of Funding
This work supported by NSF Research Grant CC7-1337131. Yizhou Zhang is also supported by the Annenberg Fellowship of the University of Southern California. We sincerely thank Professor Emilio Ferrara and his group for sharing the IRA dataset with us. Also, we are very thankful for the comments and suggestions from our anonymous reviewers.
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Checklist
1. For all authors...
(a) Do the main claims made in the abstract and introduction accurately reflect the paper's
contributions and scope? [Yes]
(b) Did you describe the limitations of your work? [Yes] We discuss about some limitations in the Ablation Test of Section 5.1.2. Specifically, the performance of our model may vary with the quality the prior knowledge, Also, the M-step tends to raise the general performance but can add false positives.
(c) Did you discuss any potential negative societal impacts of your work? [Yes] In general, our model can bring important benefits in addressing misinformation. However, since the M-step may bring more false positive cases, manual verification to the final detection result is necessary and important for avoiding harming normal accounts
(d) Have you read the ethics review guidelines and ensured that your paper conforms to them? [Yes]
2. If you are including theoretical results...
(a) Did you state the full set of assumptions all theoretical results? [Yes]
(b) Did you include complete proofs of all theoretical results? [Yes] In the Appendix.
3. If you ran experiments..
(a) Did you include the code, data, and instructions needed to reproduce the main experi- mental results (either in the supplemental material or URL)? [No] The IRA data a restricted access dataset from Twitter. Therefore, we cannot directly share it. We requested it from the Authors of this work [20), and must redirect any requests to the last listed Author. We do provide our source code. We also provide the COVID-19 Vaccine Tweets data (collected by using Twitter public APIs) in supplementary material, in accordance with Twitter policies. However, it does not have ground truth labels, but the detection and analysis results be replicated and provide details for it in the code and Appendix.
(b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] Yes, we specify the data splits the paper. Other details are included in the Appendix due to the page limits.
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(c) Did you report error bars (e.g., with respect to the random seed after running experi- ments multiple times)? [Yes] We put them in the Appendix.
(d) Did you include the total amount of compute and the type of resources used (c.g., type of GPUs, internal cluster, cloud provider)? [Yes] Due to the page limits, put them in the Appendix.
4. If you are using existing assets (e.g., code, data, models) or curating/releasing new assets...
(a) your work uses existing assets, did you cite the creators? [Yes]
(b) Did you mention the license of the assets? [Yes] In the source code
(c) Did you include any new assets either the supplemental material or as URL? [Yes]
We include the COVID-19 dataset (collected by us) in accordance with Twitter policies cww./en/deeelllpeeeeeessse
-nddloliiy...... (d) Did you discuss whether and how consent was obtained from people whose data you're 're using/curating? [Yes]
Intpps:/deeeelppe.twiieee
(e) Did you discuss whether the data you are using/curating contains personally identifiable information offensive content? [Yes] The datasets used are in accordance with Twitter APIs and policies and no personally identifiable information is utilized in the model, or presented in the paper.
5. If you used crowdsourcing conducted research with human subjects...
(a) Did you include the full text of instructions given to participants and screenshots, applicable? [N/A]
(b) Did you describe any potential participant risks, with links Institutional Review Board (IRB) approvals, if applicable? [N/A]
(c) Did you include the estimated hourly wage paid to participants and the total amount spent on participant compensation? [N/A]
14


Reviews:
[{"review_id": "ilZQi1y_Ju", "reviewer": "Reviewer_q2wM", "summary": "This paper proposes extensions to Temporal Point Processes and apply them to Coordination Detection on Social Media. The main contributions are the inclusion of a \"prior knowledge based graph\" and the corresponding Gibbs-sampling based EM Algorithm including an meanfield approximation. Empirical improvements are shown on two data sets, in one case also a short qualitative analysis is provided.", "questions": "", "limitations": "", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "Without having deep expertise in this area, I got the impression that the overall approach and application is original. In general, there are original contribution in respect to the inclusion of a \"prior knowledge based graph\" the EM optimization algorithm and its meanfield approximation. Also the empirical evaluation is extensive with a clear dominance of the proposed approach. However, in detail, the contributions are rather incremental. The \"prior knowledge based graph\" promises less than expected. The \"Cubic Function\" and \"Temporal Logic\" remain very heuristic and specific. The general contribution of this steps remains unclear. More thorough appears to be the optimization procedure although all the combined techniques (EM, meanfield, Gibbs) are well known and typically combined in that way.\n\nThe quality and clarity of the presentation and formalization is very good. The paper is easy to follow and if provides an intuitive structuring. Some figures lack resolution and/or are too small (Fig 2a, 3). Some terminology used should be better related to existing work, like \"knowledge based graph\" vs \"knowledge graph\" or \"temporal logic\" vs formal logic.\n\nMy main concern is the significance. While the proposed approach shows a great performance, it lacks insights into why this is the case. Specifically, the effect of the inclusion of prior knowledge remains unclear. First, both, the \"Cubic Function\" and \"Temporal Logic\" are shallow. Why is the function cubic? Specifically the incorporation of \"Temporal Logic\" raises high expectations, but it seems this is basically a single and very simple inequation to rule out some account pairs. There are no Temporal Logic formulas or any sort of deeper integration. Also, the impact of this \"prior knowledge based graph\" remains unclear, since I couldn't find an no ablation study showing what is lost without it.", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "Coordinated misinformation campaigns have societal impact. The proposed method tries to mitigate it. Still the impact of such a method should be discussed, since there are also risks, like false positive classifications.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "2"}, "extra_text": {}}, {"review_id": "NPOXt1R7vX2", "reviewer": "Reviewer_neqB", "summary": "The authors tackle an important problem of identifying coordinated accounts on social media, which could help prevent misinformation propagation. Different from previous works, this paper focuses on incorporating prior knowledge into neural temporal point process model via variational inference framework. The authors show the improvements over the state-of-the-art method AMDN-HAGE on one dataset. ", "questions": "", "limitations": "", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "The authors propose to incorporate prior knowledge into neural temporal point process model for coordination detection. However, the definition of prior knowledge, which is an important concept used in this paper, is not clearly introduced. What kind of knowledge is defined as prior knowledge?  \n\nSecond, in the abstract, the authors state that existing works that incorporated prior knowledge suffer from limited expressive power and poor performance. Such a statement is an important motivation of this paper, but the related works section does not include comparison with respect to such relevant work. Therefore, the current related works section cannot clearly depict the difference between the proposed way of using knowledge with that adopted by previous approaches. \n\nThird, the proposed method is closely related to the existing work AMDN-HAGE, which is used to initialize the embedding layer in this paper.  How is the performance of the proposed method without this initiation? This experiment would be needed to justify the contribution of the proposed techniques. In the current experiments, the proposed method utilizing prior knowledge is compared against other methods that do not incorporate prior knowledge. This comparison may only illustrate the important role of the prior knowledge but cannot justify the need of adopting the proposed model that incorporates knowledge in this specific way. Moreover, the authors only show the performance comparison on one dataset, which may not be enough to support the statement on the effectiveness of the model. In AMDN-HAGE, the paper includes the comparison in the supervised setting. Since supervised learning setting is very common and widely used, is there any specific reason not to show experimental results in this setting?", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "No, the authors did not discuss about how to handle failed cases. More discussions about potential social impact and failed modes may need to be added to the paper. ", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "4"}, "extra_text": {}}, {"review_id": "7bXMjlv5FpW", "reviewer": "Reviewer_insa", "summary": "This paper provides an approach to detect coordinated groups of activity from social media. It defines a distribution over group assignments, and a function to measure how it matches the account embeddings and prior knowledge.  The main novelty is integrating prior into another recently proposed method for this task. The prior accounts for co-activity, interacting with the same tweet and time overlap of the activity. This information is integrated into the neural temporal point process.\n", "questions": "", "limitations": "", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "\nThe related work overlooks a large body of work from the Graph Mining community on dense block detection. This could be a good entry and a potential baseline:\nHooi, Bryan, et al. \"Fraudar: Bounding graph fraud in the face of camouflage.\" Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016.\n\nThe argument on graph-based detection ignores that social media data comes in naturally as graphs, i.e. the interactions between the users. It argues that the graph needs to be defined based on similarity or learned, whereas you can directly model the data as a temporal graph. K-means over (temporal) graph embedding methods, should have been another baseline.  Another baseline could be a tensor/matrix factorization method.\n\nOverall, the paper is weak in terms of related works, and baseline and evaluations. There is a limited set of baseline used. Considering there available datasets are limited for benchmarking, there could be synthetic experiments, e.g. with SBM, to better test the method. And/or, more in-depth analysis of the real-world dataset studied. As is, the results are not convincing. Also, how does it compare with what 25 reported? Is the data the same? \n\n\nOn a more high-level note, how would you differentiate between a group of users discussing the same topic heavily, say an election, and a coordinated group? Many of the detected hashtags in the covid example, are related to Canadian politics, cdnpoli, ableg, abpoli, onpoli. It seems to me, the method is detecting groups, which can be coordinated or not. There is also very limited information given on how the data is collected, timeframe, keywords, etc. for the experiment. \n\nOn the novelty also, there could be a better separation from 25, as it is not clear which parts of the paper are novel in this submission. The datasets, experiments, and the model seem to be very similar. \n\nFigure 2a is not very readable.\nPage 8, line 299: We use topic mining> please elaborate what method was used.\nPage 9, line 309: we measure percentage of unreliable and conspiracy news sources shared in the tweets> please explain how this is measured\nOn Table 1, why is the reported performance for baseline lower than what is originally reported in 25, how is the setting different? Those results look more competitive.\n\n", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "potential negative societal impacts of your work are declared none. However, if applied in practice, this method can be used to label users as suspicious, coordinated, and get them banned from the platform. There could be more thought into how the method could be used and the implications. \n\n", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "5"}, "extra_text": {}}, {"review_id": "2vv3b64mOO", "reviewer": "Reviewer_aBu9", "summary": "This work studies the problem of detecting coordinated user activity in social media. \nA recent paper by Sharma et al., 2020 [(arXiv:2008.11308)](https://arxiv.org/abs/2008.11308) proposed AMDN-HAGE -- an approach for coordination detection based on neural TPPs.\nSpecifically, AMBN-HAGE trains a neural TPP model on traces (e.g., retween sequences) and treats each user/account as an individual mark in a marked TPPs.\nThen, mark embeddings learned by the model are clustered with K-means to detect accounts exhibiting similar activity (cluster = group of coordinated accounts).\n\nThe submitted paper extends AMDN-HAGE by incorporating prior knowledge (e.g., the fact that coordinated accounts are active during the same time periods).\nThis is achieved by treating the cluster assignments  as latent variables in a Markov random field (MRF).\nThe MRF construction takes into account the mark embeddings of the neural TPP model and pairwise similarities between accounts derived based on domain knowledge.\nBoth parameter learning and cluster inference are done simultaneously by maximizing the ELBO. Since the naive formulation of the ELBO is computationally intractable, the authors derive a lower bound that enables an efficient approximation.\nThe experiments on real-world data show that the proposed approach provides a consistent improvement over the baselines, both in unsupervised and semi-supervised detection of coordinated accounts.", "questions": "", "limitations": "", "rating": 6, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "**Originality:** The proposed method is sufficiently different from AMDN-HAGE. The idea of clustering the marks and learning a TPP model simultaneously has not been explored in neural TPP literature, as far as I know. Though, I am not fully familiar with other literature on coordination detection, so I might not be aware of some related works.\n\n**Quality:** The approach is theoretically sound. I wasn't able to find any mistakes in the derivation of the main theoretical result (except minor inconsistencies in notation that are listed below). The experimental evaluation is solid, and several ablation studies demonstrate that different model components indeed improve the detection quality. There are some baselines that I believe could be included in the evaluation to make the results even more convincing (see questions below).\n\n**Clarity:** The proposed model is described clearly and the paper is easy to follow. The detailed description of the experimental setup should make the results easy to reproduce.\n\nTo summarize, this work proposes an interesting approach to a relevant task, and I haven't found serious flaws in the paper. However, I would like to see the following questions addressed during the discussion period.\n\n**Questions:**\n1. Have you compared to baselines that operate exclusively on the \"graph constructed from prior knowledge\" (Section 4.1)? \nSince the VigDet model combines two information sources (embeddings + graph), it would be interesting to see how a model that only considers each source separately performs. AMBN-HAGE covers the \"embedding only\" case, but the \"graph only\" case is missing. For example, one could perform mean-field VI on the MRF model that only includes the pairwise potentials (Equation 6). Other approaches, such as dense subblock/subgraph discovery could also be applied to the similarity matrices constructed in Section 4.1 (something similar to \"M-Zoom: Fast Dense-Block Detection in Tensors with Quality Guarantees\", ECML-PKDD 2016 and references therein, but for matrices instead of tensors).\n\n2. How can we be sure that the model is not simply detecting communities of users in a social network? Have you considered any datasets where both coordinated groups as well as legitimate communities are present? For instance, machine learning researchers often like/retweet a similar set of tweets, but, intuitively, they shouldn't be considered as coordinated accounts.\n\n3. How does the proposed approach address the power-law distribution challenge (Figure 1b)?\n\n\nMinor suggestions:\n- Lines 16 & 66: I find the usage of the phrase \"theoretical guarantee\" somewhat confusing. Potentially, something along the lines of \"we derive a lower bound that allows us to efficiently approximate the ELBO\" would be clearer.\n- Theorem 1 (Equation 12)  \n    - It would be helpful to specify that the constant terms are constant w.r.t. $\\theta$ and $E$\n    - Why include the $\\sum_{1 \\le m^\\prime \\le M} \\exp (...)$ term (the denominator) inside the expectation? The term is independent of $Q$ and could simply be computed outside of the expected value, if I understand correctly.\n- Line 106: In my experience, $dN(t)$ is usually referred to as the differential, not derivative. See e.g. these lecture notes http://courses.mpi-sws.org/hcml-ws18/lectures/TPP.pdf\n- Line 125: The kernel function $\\phi$ is usually periodic but not translation invariant, in general it doesn't hold that $\\phi(x) = \\phi(x + y)$ for all $x, y$.\n- Line 171: `$Y$` instead of `Y`.\n\n- Appendix:\n    - Equation 3: Inconsistent usage of $y$ and $Y$\n    - Equation 5: $Y^\\prime$ instead of $Y$", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "A short discussion of limitations is provided. Potential negative societal impacts are not discussed.", "review_text": "", "extra_scores": {"needs_ethics_review": "No", "ethics_review_area": "", "time_spent_reviewing": "4.5"}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper proposes a new approach to detect coordinated accounts in social media via neural TPPs. Reviewers highlighted that the paper proposes an interesting approach to this important problem and, furthermore, shows promising results. However, based on the current manuscript, reviewers raised concerns with regard to clarity (e.g., differentiation of coordination from normal user behavior/communities, modeling details), discussion with respect to related work, novelty, and experimental evaluation (baselines, datasets, ablations). After rebuttal and discussion, three knowledgeable reviewers indicated therefore to reject the current manuscript while one reviewer indicated weak support for acceptance. Although the paper proposes interesting ideas, I agree with the majority of reviewers that the manuscript is currently not ready for acceptance at NeurIPS due to the aforementioned concerns and would require an additional revision (e.g. with regard to clarity, experimental evaluation, as well as analyses that provide insights into coordination vs community). However, I encourage the authors to revise and resubmit their work based on the feedback from this reviewing round.
Decision: Accept (Poster)