Counterfactual Neural Temporal Point Process for Misinformation Impact Estimation on Social Media
Anonymous Author(s)
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Abstract
Recent years have witnessed the rise of misinformation campaign which spreadspecific narratives on social media to manipulate public opinions on different areas,such as politics and healthcare.
Consequently, an effective and efficient automaticmethodology to estimate the impact of the misinformation on user beliefs andactivities.
However, existing works on misinformation impact estimation eitherrely on small-scale psychological experiments or can only discover the correlationbetween user behaviour and misinformation.
To address these issues, in this paper,we build up a causal framework that model the causal effect of misinformationfrom the perspective of temporal point process.
To adapt the large-scale data, wedesign an efficient yet precise way to estimate the ITE via neural temporal pointprocess and gaussian mixture models.
Extensive experiments on synthetic datasetand real-world dataset verify the effectiveness and efficiency of our model.
Introduction
Recent researches reveals that widespread fake news and misleading information have been exploitedby misinformation campaigns to manipulate public opinions in different areas, such as healthcare[23,25 , 12] and politics[13].
To address this crucial challenge, research efforts from different perspectiveshave been devoted, such as fake news detection and coordination detection[22, 23, 25].
However, an essential associated research question has not been explored sufficiently: how to know apiece of misinformation’s causal influence on a user’s beliefs and activities on a large-scale socialmedia.
Precisely estimating such impact is crucial for delivering the corresponding clarificationcontents to the users that are most likely to be affected.
Nevertheless, most of existing researches insocial media analysis focus on understanding correlation between misinformation and user activities,rather than causal effect [25, 12, 28].
As a result, they can not distinguish the effect from personalprior beliefs and engagement with misinformation.
Current researches on misinformation’s causalinfluence on people are mainly from psychology field [11, 26].
They are usually based on carefullydesigned psychological randomised controlled trials on recruited subjects.
Thus, it is impossible toextend them onto large-scale social media platforms due to the high cost to recruit enough subjectsand ethical risk in conducting such a large-scale psychology experiments.
Since personal beliefs are usually unobservable, researchers usually apply the feature of the tweetsgenerated or retweeted by the users as a proxy [25].
However, the lack of appropriate algorithmictools to conduct causal analysis on social media posts prevents researchers from understandingcausal effect of misinformation.
The processes that social media users generate original posts andengage with existing posts are typical temporal point processes.
But existing methodologies for temporal causal effect estimation mostly focus on covariates and outcomes continuously distributedon timeline [4, 3, 2], rather than discrete event points randomly scattered on timeline.
Although theessential theory for counterfactual analysis of point process is already established[19], most worksare motivated by healthcare and thus focus on the hazard models, e.g. survival analysis [1] or thechance to catch cancer [20], which only consider the single occurrence of the most recent future event.
However, on social media, we care more about multiple events happening in a time window.
[10] is arare work studying the causal effect on multiple occurrences in temporal point process.
But one of itsassumption is that the event marks must be categorized to a finite number of classes, leaving no spacefor the rich continuous features of social media posts, such as user sentiment and subjectivity scores.
In this work, we propose a framework that models the causal effect of a given piece of informationon user beliefs and activities via counterfactual analysis on temporal point process with continuousfeatures.
We first define a causal structure model that characterizes the misinformation impact as howthe engagement with the misinformation change a user’s intensity function of generating originalposts.
In this model, the engagement with misinformation is considered as the treatment, and theuser’s future conditional intensity function is considered as the outcome.
Then we design a functionalthat converts the change of two functions to a vector with intuitive physical meaning.
To estimatethe effect, we design a neural temporal point process model.
It disentangles the distribution ofevent timestamp and post feature (e.g. the text embedding).
Then it models the distribution of postfeatures and event timestamp with Gaussian Mixture Model and uni-variate temporal point processrespectively.
Such design enables it to acquire a closed-form solution of the feature expect without aloss of expressive power, leading to a well balance between precision and inference time complexity.
A critical challenge in training neural networks to recognize causal effect is the hidden bias in thedataset.
In social media data, the most crucial bias is from information cocoons [32]: users tend moreto engage with the contents that they are interested in, and thus personalized recommendation systemswill deliver user more contents that they are interested in to increase user engagement.
Such biasleads to a data distribution different from randomised controlled trials and thus make neural networksgive biased estimation.
To decorrelate time-varying treatment from user’s covariates and history inpoint process, we apply adversarial training to optimize a min-max game.
More specifically, theencoder tries to minimize the likelihood of the observed treatments while a treatment predictor triesto maximize it.
Our theoretic analysis proves that any balanced solution of the min-max game, ratherthan the global optimal solution in existing works [4], can help us remove the bias from informationcocoons.
The extensive experiments on synthetic dataset and real-world datasets also indicate thatour framework is able to acquire unbiased and identifiable estimation to the causal effect.
In conclusion, the main contributions of the proposed model are as follows:
• We propose a novel research problem on misinformation impact, which aims to find the
causal effect of misinformation on users’ belief and activities on social media.
• We propose a causal structure model to quantify the causal effect of misinformation and further design a neural temporal process model to conduct unbiased estimation to the effect.• We evaluate our model on synthetic datasets to examine its effectiveness and efficiency andues it to recognize identifiable causal effect of misinformation from real-world data.
Related Work2.1 Influence of Misinformation
Recent researches about misinformation mainly focus on detecting fake news[22, 17, 21], misinformation campaign detection [24, 31] and understanding how fake news attract user engagement[7].
Some researcher attempts to study the relation between misinformation on people’s behaviours[11, 26, 25, 12].
However, most of them focus on mining the correlation between misinformationand people behaviours rather than causal effects.
Only a limited amount of works, such as [26] try tounderstand the causal effect.
However, they are usually from psychology field, and mainly rely on carefully designed randomised controlled trials.
Extending such trials on large-scale social mediaplatforms brings not only high cost but also potential ethical risk.
2.2 Temporal Point Process
The process that a user retweet or posts tweets can usually be modeled as a temporal point processwith event feature [24].
A temporal point process (TPP) with event feature is a stochastic processwhose realization is a sequence of discrete events in a continuous timeline: S
=
[( f 1 , t 1 ) , ( f 2 , t 2 ) , ... ] ,where f 1 is the event feature (a scalar or a vector) and t is the timestamp of the event.
A TPP is fullycharacterized by an intensity function λ ( f , t | S h ) defined in the following integral equation:
[Equation]
where F is an area in the feature space, S h is the historical sequence of all events happening beforetime T 1 , N ( F , T 1 , T 2 ) is the number of events whose feature vectors are in F and timestampsare in the range [ T 1 , T 2 ] .
The meaning of λ ( f , t | S h ) is the expected instantaneous speed that theuser generate posts at point f in the feature space on time t .
The process after time t i is fullydescribed by λ ( · , ·| S h ) [6].
Recent works propose to apply neural networks to model the λ function[8, 15, 30, 33, 16, 6].
2.3 Counterfactual Analysis on Temporal Point Process and Continuous Time Series
A previous work[10] proposes a counterfactual analysis framework to understand the causal influenceof event pairs in temporal point process.
It defines the ITE of an event toward future process as:
[Equation]
where µ y is the expect of the event number of type y per unit time in time range [ t, t + T ] .
µ 1 y indicatethe case that a treatment is applied (exposed to misinformation) and µ 0 y is in contrary.
However,this metric is only suitable in the case that the events can be categorized to finite discrete types.
This is not applicable for social media post because most meaningful features of the posts, such asgeographical information, sentiment score and subjective score, are naturally continuous.
Forciblydiscretizing them will lose meaningful information.
Besides, for counterfactual analysis of timeseries, [4] proposes CRN, a neural model that can learn unbiased estimation to counterfactual worldand causal effect.
[3] proposes to analyze counterfactual estimation using synthetic controls viaa novel neural controlled differential equation model.
[2] introduces a new causal prior graphto avoid the undesirable explanations that include confounding or noise and use a multivariate
Gaussian distribution to model the real continuous values.
However, all of them focus on modelingan observable variable existing on a continuous timeline instead of temporal point process.
Unlessgetting heavily revision, such previous work can not be simply transferred to our problem scenario.
Proposed Causal Structure Model and Treatment Effect
3.1 Causal Structure Model
In this study, we focus on understanding how a user’s engagement with the misinformation postcausally influence the characters of the posts generated by the user in a fixed future time window.
Weformulate the process that a user interact the post shared by others and generate social media posts astwo temporal point process where each event carries a continuous outcome vector.
We denote theprocess of engaging the diffusion of a post as P e and the process generating new posts as P g .
Thenthe realization of the two temporal point process are respectively two sequences S e and S g of discreteevents with continuous outcome vector in a continuous time range:
[Equation]
[Figure]
where f is the feature vector characterizingthe event and t is the time stamp.
For S in ,each event correspond to an interaction (e.g."like“ or comment), and the vector f ( e ) is thefeature of the content (like the text representation, sentiment score and metadata) fromothers.
Similarly, for S g , the vector f ( g ) is thefeature of the content generated by the user.
To examine the causal effect of an interactionevent on the posts generated by the user in thefuture, we construct the following causal structure model, formulated as < X, Y, Tr > , where X is the covariate,
Y is the outcome and Tr is the treatment.
In this model, given an interactionevent ( f ( e ) i , t ( e ) i ) whose causal effect is to be examined, we consider the this event as the treatment Tr , and all the events, including both engagement events and posting events, that happen beforet ( e ) i are considered as the covariates.
As for the outcome, rather than simply considering the mostnext generation event after t ( e ) i , we need a representation that can reflect the change of the wholegenerating process S g in a fixed future time window T .
Thus we apply the conditional intensityfunction of the future process, denoted as λ ( f , t | Tr ∪ X ) , as outcome.
As discussed in the relatedwork section, λ function completely describe the future process.
The overview of the model ispresented in Figure.
3.2 Treatment Effect Evaluation
In traditional counterfactual analysis works, the outcome is usually a scalar or a vector with finitedimensions.
Thus, the treatment effect can be trivially computed by compare the difference of theoutcomes from real world and counterfactual world.
However, in our framework, it is non-trivial tocompute the difference of two functions.
To overcome this challenge, we propose to first apply afunctional F to project the λ function to a vector with finite dimensions:
[Equation] [Equation]
[Equation]
where P ( S | λ, Tr ∪ X ) is the distribution of the event sequence S sampled from the temporal pointprocess described by λ ( · , ·| Tr ∪ X ) , T is the time window that is a hyper-parameter, sup ( f ) is thesupport set of f (the area where the probability density is larger than 0), and S t : t + T is a sub-sequenceof S .
S t : t + T contains every event in S that happens at a time between t and t + T .
The intuitivemeaning of F is the expected mean feature vector of all posts generated by a user.
Thus, by comparingthe outputs of F in real world and counterfactual world, we can see how the engagement with aspecific post change the average features, e.g. general sentiment scores or text embedding.
With thisfunctional, we can simply compute the individual treatment effect as:
[Equation]
where ∅ is an empty set, F T ( λ, ∅∪ X ) is the functional from counterfactual world in which we assumethat the treatment is not applied (e.g. the misinformation post is not recommended or labeled asmisinformation).
For brief, we write F T ( λ, ∅ ∪ X ) as F T ( λ, X )
The overall impact of the treatmentcan be represented with the average treatment effect:
[Equation]
where U is the set of users who engaged with the treatment post.
3.3 Treatment Effect Calculation
In the above sections, we define the causal structure model and the treatment effect.
However, theabove formulas are hard to compute.
Therefore, in this subsection, we will derive a computableformulation of the treatment effect.
We will start from the following theorem:
Theorem 1.
For a user u , if the intensity function λ ( f , t | Tr ∪ X ) is known, then we have:
[Equation] [Equation]
The first equation can be trivially proved by replacing the F in Equation 1 with the support set.
Thesecond one can be proved with the Campbell’s Theorem [5].
A detailed proof is provided in theappendix.
The above formulas contain double integral, which is inefficient to compute.
To transformthe double integral to a single integral, based on a previous work in spatial-temporal point process[6], we have:
[Equation]
Thus, we can disentangle λ ( f , t ) and respectively model λ ( t i ) and p ( f | t ) .
More importantly, we cansimply model µ as:
[Equation]
And with this formula, we have:
[Equation]
The above formulas contain only single integrals.
Thus, they can be efficiently approximated withsummation: (cid:82) x
[Equation]
Neural Estimation of Treatment Effects
The above section construct a causal framework that can measure the impact of a given social mediapost based on the change of λ ( t | Tr ∪ X ) and p ( f | t, Tr ∪ X ) .
In this section, as shown in Figure 2we will further discuss how to estimate the impact with a neural temporal point process model.
4.1 Learning Conditional Intensity Function via Maximum Likelihood Estimation
The log-likelihood of an observed event ( f, t ) (no matter an engagement event or an generation event)can be written as:
[Equation]
(14) where t n is the timestamp of the last event in the set
Tr ∪ X .
The above equation provides us with away to learn λ ( t | Tr ∪ X ) and p ( f | t, Tr ∪ X ) by maximizing the likelihood of each event given thehistorical information (treatment and covariates).
To enable the model to make correct prediction forboth λ ( f , t | Tr ∪ X ) and λ ( f , t | X ) , we construct two kinds of samples to train the functions:
Engage
Post Post
Engage Post 𝑌,𝑇𝑟 ∪𝑋 𝑌,∅∪𝑋 𝑇𝑟,𝑋
Share Encoder (H) 𝐻(𝑇𝑟 ∪𝑋) 𝐻(𝑋) 𝐻(𝑋)
Intensity
Function
Feature
Distribution
GRL
Treatment
Predictor 𝑝 𝒇 𝐻(𝑇𝑟 ∪𝑋) ,𝑡 , 𝑝(𝒇|𝐻(𝑋),𝑡) 𝜆 𝑡 𝐻(𝑇𝑟 ∪𝑋) , 𝜆(𝑡|𝐻(𝑋)) ̂𝑝(𝑇𝑟|𝐻(𝑋))
Adversarial training
Samples with valid Treatment : If for a generating event ( f ( g ) , t ( g ) ) , its most recent previous event is an engagement event ( f ( e ) , t ( e ) )
(in other words, the user does not have otheractivity between the engagement event andthe generating event), then we can construct asample ( Y, Tr ∪ X ) , where Y =
( f ( g ) , t ( g ) ) ,
Tr =
( f ( e ) , t ( e ) ) , and X is a sequence thatcontains all engagement events and generationevents before Tr .
Samples without Treatment : If for a generating event ( f ( g ) , t ( g ) ) , its most recent previous event is still a generating event (in otherwords, the user generate two original postswithout engaging with other posts), then wecan construct a sample ( Y, X ) , where Y =( f ( g ) , t ( g ) ) and X is a sequence that containsall engagement events and generation events before Y .
In this sample, between the last generationevent in X and Y , there is no interruption from treatment.
Thus, it helps the model to learn λ ( t | X )and p ( f | t, X )
For a sample ( Y, Tr ∪ X ) or ( Y, X ) , we first use a shared encoder H ( · ) to project ( Tr ∪ X ) (or X )to a representation vector h
= H ( Tr ∪ X ) (or h = H ( X ) for the case where treatment is NULL).
Then we model the intensity function and feature distribution as λ ( t | h ) and p ( f | t, h ) .
For λ ( t | h ) ,following FullyNN, we use a multi-layer perceptron MLP ( h, t ) to model its integral (cid:82) t t n λ ( t | h ) dt .
The MLP’s partial derivative with respect to t is λ ( t | h ) .
To model p ( f | t, h ) , a straightforward solution borrowed from generative deep learning is to applya neural network, i.e. the decoder, to transform a simple distribution, e.g. a Gaussian distributionwhose parameters are decided based on h and t , to a complicated distribution.
The decoder can betrained via different loss function, like reconstruction error (variational auto encoder) and likelihood(normalizing flow) 2 [27, 6].
However, this method has an important drawback: its conditional expect 218 E [ f | t, h ] does not have a closed-form solution.
To compute the expect, we can only apply sampling orapproximation, e.g. forwarding the expect of the simple distribution into the decoder 3 .
To address theabove challenge, we propose to explicitly model p ( f | t, h ) with a mixture of Gaussian distributions:
[Equation]
[Equation]
where w j is the mixture weight, σ j is a scalar, u j is a vector with the same dimension as f , andg ( · ) is a standard multivariate gaussian distribution N ( 0 , I ) whose covariant matrix is an identicalmatrix.
Although the formula of each component is simple, their mixture has a theoretical guaranteeon universal approximation to all distributions [29].
Because the expect of a mixture distribution isthe mixture of the expects, we have a closed-form solution for E [ f | t, h ] :
[Equation]
4.2 Adversarial Balanced Neural Temporal Point Process
As discussed in the related work section, by maximizing the likelihood of the posts generated bythe users, we can train a neural network that predict λ ( t | Tr ∪ X ) and p ( f | t, Tr ∪ X ) .
However,
Because the decoder D is a non-linear function, E [ D ( x )] is usually different from D ( E [ x ]) previous works have proved that if we do not balance the bias from the correlation between treatmentand covariates, the model will tend to give biased prediction and thus can not give precise estimationof the treatment effect.
A crucial bias in social media data is information cocoon: personalizedrecommendation systems will deliver user the contents that they are interested in.
For example, it willdeliver more anti-vaccine posts to anti-vaccine users because they are more likely to be interested inthose contents.
As a result, the anti-vaccine users will engage more with anti-vaccine posts.
To address the above issue, following previous works in neural counterfactual prediction, we applydomain adversarial training to learn a representation h that is invariant to such a bias[4].
Morespecifically, we hope to learn a encoder H such that for any two users with different history
X 1and X
2 , p ( Tr | H ( X 1 ))
=
p ( Tr | H ( X 2 )) for the same Tr .
In other words, in the representationspace, the probability that the two users interact with the same post at the same time should be same,which is the same as psychology experiments that divide the experimental and controlled groupsrandomly.
To achieve this object, we apply adversarial training to remove the information aboutfuture treatment from the representation of covariates.
More specifically, we additionally train atreatment predictor ˆ p ( Tr | H ( X )) by modeling λ tr ( t ( e ) | h ) and p tr ( f ( e ) | t, h ) with the encoding h ofthe historical covariates X .
However, between the treatment predictor and the encoder, we insert agradient reversal layer (GRL)
[9, 18, 4] to reverse the sign of the gradient.
Thus, when we optimizethe treatment predictor to maximize the likelihood of the observed treatment Tr = ( f ( e ) , t ( e ) ) , the
GRL will make the encoder to minimize the likelihood.
This process leads to the following min-maxgame:
[Equation]
The following theorem provide theoretic guarantee that the above adversarial training reduce the biasintroduced by treatment-covariate correlation (e.g. recommendation system and personal interest):
Theorem 2.
Given the following min-max game:
[Equation]
the min gamer’s Nash balanced solution H ∗ , ensures for any X 1 , X 2 , the following equation holds:
[Equation]
where p denote the ground-truth conditional distribution of treatment given encoding.
Experiments
On real-world social media platforms, the ground truth causal effects of user engagement with posts,no matter misinformation post or information post, are unknown.
To address the unknown groundtruth causal effect, previous works of causality analysis evaluate their models on synthetic dataset.
In this paper, following previous works, we evaluate the performance of our model and compareit with baselines on synthetic dataset.
Then we apply our proposed method to evaluate the impactmisinformation on a real-world social media data about COVID-19 vaccine collected from Twitter.
5.1 Experiments on Synthetic Data
Synthetic Data Generation:
To simulate the real-world social media, we generate 15000 users and120 post of news.
Each user i is represented with a hidden vector u i , which correspond to the statusof a social media user.
Each piece news n has two randomly generated feature vectors: a topic vectorv topic ( n ) and an inherent influence vector v in ( n ) .
Each user has two kinds of activities: (1) engagingwith one of the 120 news post and (2) posting a post with original contents.
The chance that a userengage with a post is decided by v in ( n ) and u i , simulating information cocoons .
Engagement eventwith news post n will change the hidden status u of the user.
The scale and direction of the changeare decided by the current user status, the topic vector and the inherent influence vector jointly.
Foreach user, the engagement events and the posting events are modeled through two Hawkes processrespectively.
Both Hawkes processes are influenced by user status u .
Also, the feature of each postingevent f is drawn from a distribution P ( f | u, t ) characterized by a random parameterized multi-layer
[Table]
perceptron (MLP) taking ( u, t ) and random noises as input and output f .
Thus, the engagement withthe news post will have causal effects on the two processes.
Since we have all parameters of themodel, we can calculate the ground-truth ITE defined in Eq.
7 for the synthetic dataset.
The detailsof the data generation algorithm is included in the Appendix.
Baselines: To the best of our knowledge, the causality effect on temporal user behaviour frommisinformation is not explored by previous works.
Thus, we are lack of well-established baselines forthis specific task.
To address this issue, we select some baselines from previous works on temporalpoint process and temporal causal inference.
We extend them to adapt our setting.
The baselinesinclude two kinds:
Non-causal neural temporal point process that predict the user future behaviours without considering causal effect.
We select FullyNN [16] as representative of this kind as it has the same neuralarchitecture as our model.
It can also be regarded as our model’s variant w/o adversarial balancing .
Causal neural temporal point process that aims at discovering causal effect of event pairs intemporal point process.
This kind includes CIP [10] and CRN-VAE [4] (we heavily revise theoriginal model CRN from the paper so that it can adapt our setting).
CRN-VAE can also be regardedas ablation variants of our work that uses VAE to model p ( f | t, h ) .
Since VAE does not have a closedform solution of feature expect, we report the results applying sampling and approximation separately.
In this work, we will evaluate the proposed model in two aspects:
[Table]
ITE Estimation : we will evaluate themodel by comparing ITE estimated by themodel and the ground-truth ITE.
We reportthree metrics: accuracy (the model needto correctly predict whether the engagement increase or decrease each dimensionof the expected average feature), Relative
Absolute Error (RAE) and Relative Root
Square Error (RRSE).
In addition, we alsoreport the inference time of our model toreflect our model’s efficiency.
As shown in Table 1, our model establishes new state-of-the-art on all three quantitative metrics.
Thismeans that our model can fully utilize the causal information from the multivariates point process forunbiased treatment effect estimation.
In particular, the model with causal analysis outperforms themodel with direct neural network prediction (FullyNN), which leads to results that are not causallyrelated.
Simultaneously, our model outperforms all baselines in all three metrics of estimationprecision.
Although CRN-VAE incorporated with sampling method can achieve a performance veryclose to us, it spends significantly longer inference time.
Causal Effect Inference : CIP defines the treatment effect in a way different from our model.
Thus
ITE estimation experiment is not fair for it.
For a fair comparison, and also to further prove that ourmodel can achieve unbiased estimation, we use all the models to predict the ATE of each news post.
Then we evaluate the correlation between the learnt ATEs and ground-truth average change of thenews post to all users’ hidden statuses.
The more correlated the learnt ATE is, the better it reflect theinherent causal effect of the engagement on users.
To evaluate the correlation, we apply the followingtwo metrics
From Table 2, the ATE of our model best reflect the ground-truth average change of the news post onthe simulated data for both two evaluation metrics.
This suggests that our model has the potential todiscover the influence of misinformation on social media users’ hidden status, e.g. interest and idea.
5.2 Experiments on Real World Data
In this section, we apply our proposed model on the Twitter dataset to estimate misinformationimpact on social media scenario.
We apply the data set collected in [31, 25], including a total of16,9008 tweets from 24,192 users over a 5-month period from 2020/12/09 to 2021/04/24.
Notably, wefocus on understand how the tweets that users retweeted influence their behavior of posting originaltweets.
For each post, its feature f includes: text representation (extracted with a pre-trained BERT),sentiment score and subjectivity score.
We discover the following two phenomenons with our model.
[Figure]
Identifiability between misinformation and information in influencing people’s narratives :
For the desired outcome, we analyze the distinguishability between "retweeting fake news" and"retweeting true news" events.
More specifically, for each retweeting event, we use its ITE estimatedwith our model as feature (dimension reduced via PCA [14]) and whether the content is informationor misinformation as label.
As shown on Figure 3(b), we can verify the identifiability of our proposedmethod as the treatment effect of two types of the news are significantly different.
This discovery notonly supports that misinformation and information influence people’s behaviour in different ways,but also provides us with a new paradigm to detect fake news.
Misinformation is hurting people’s subjective emotion related to COVID vaccine : To understandthe influence of misinformation in a more intuitive way, we analyze the impact of retweeting eventson users’ average sentiment score and subjectivity score in the future.
The higher subjectivity thecontent gets, the more personal opinions rather than factual information text contains.
Then, weuse the proposed model to generate the estimated ITE for each retweeting event and plot the resultsof fake news and real news.
As shown on Figure 3(a) (x-axis for sentiment score and y-axis forsubjectivity score), we find that both information and misinformation do not significantly influencethe users’ subjectivity.
However, information tends to make people optimistic about vaccines (truenews increases the sentiment score), while fake news tends to make people feel negative aboutvaccines.
This discovery strongly supports the hypothesis that misinformation is hurting people’ssubjective emotion toward COVID-19 vaccines, and suggests that misinformation could be causallyresponsible for vaccine hesitancy.
Conclusion
In this paper, we propose a framework to describe the causal structure model and causal effectabout how misinformation influence online user behaviours.
We further design a neural temporalpoint process model to conduct unbiased estimation on the causal effect in a data-driven approach.
Experiments on synthetic dataset verify the effectiveness and efficiency of our model.
We furtherapply our model on real-world dataset from Twitter and recognize identifiable causal effect ofmisinformation.
However, it is also noticeable that our model is a statistical machine learning model.
Consequently, all of its estimation can only be regarded as a reference rather than judgement.
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[N/A]
[Table]
(b) Did you describe any potential participant risks, with links to Institutional Review
[Table]
Board (IRB) approvals, if applicable?
[N/A]
[Table]
(c) Did you include the estimated hourly wage paid to participants and the total amount
[Table]
spent on participant compensation?
[N/A]
[Table]
A Appendix
[Table]
Please see the supplementary material.
[Table]