Multiview Clickbait Detection via Jointly Modeling Subjective and Objective Preference
Authors: Chongyang Shi
Yijun Yin
Qi Zhang
Liang Xiao
Usman Naseem
Shoujin Wang
Liang Hu
Venue: EMNLP 2023 Findings (2023)
Keywords: Clickbait Detection
Subjective Feeling
Objective Content Relevance
Heterogeneous Dynamic Graph Network
TL;DR: A clickbait detection model that simultaneously captures subjective and objective preferences, utilizing subjective feeling features and objective content relevance to detect clickbait.

Abstract: Clickbait posts tend to spread inaccurate or misleading information to manipulate people's attention and emotions, which greatly harms the credibility of social media. Existing clickbait detection models rely on analyzing the objective semantics in posts or correlating posts with article content only. However, these models fail to identify and exploit the manipulation intention of clickbait from a user's subjective perspective, leading to limited capability to explore comprehensive clues of clickbait. To address such a issue, we propose a multiview clickbait detection model, named MCDM, to model subjective and objective preferences simultaneously. MCDM introduces two novel complementary modules for modeling subjective feeling and objective content relevance, respectively. The subjective feeling module adopts a user-centric approach to capture subjective features of posts, such as language patterns and emotional inclinations. The objective module explores news elements from posts and models article content correlations to capture objective clues for clickbait detection. Extensive experimental results on two real-world datasets show that our proposed MCDM outperforms state-of-the-art approaches for clickbait detection, verifying the effectiveness of integrating subjective and objective preferences for detecting clickbait.

Multiview Clickbait Detection via Jointly Modeling Subjective and Objective Preference
Chongyang Shi', Yijun Yin', Qi Zhanga)
Liang Xiao', Usman Naseem,, Shoujin Wang', Liang Hu²
'Beijing Institute of Technology, China Tongji University, China
'Jaees Cook University, Australia *University of Technology Sydney, Australia edu. cn
l scy_shi, yin1jun, patrickxiao Jobit edu. cn,
Abstract
It aims at automatically detecting clickbait to help users quickly assess the credibility of the informa- tion on social media.
Clickbait posts tend to spread inaccurate or mislcading information to manipulate people's attention and emotions, which greatly harms the credibility of social media. Existing click- bait detection models rely on analyzing the ob- jective semanties in posts correlating posts with article content only. However, these mod- els fail to identify and exploit the manipula- tion intention of clickbait from user's subjec- tive perspective, leading to limited capability explore comprehensive clues of clickbait. Therefore, to bridge such a significant gap, we propose multiview clickbait detection model, named MCDM, model subjective and ob- jective preferences simultaneously. MCDM introduces two novel complementary modules for modeling subjective feeling and objective content relevance, respectively. The subjec- tive feeling module adopts user-centric ap- proach to capture subjective features of posts, such as language patterns and emotional incli- nations. The objective module explores news elements from posts and models article con- tent correlations to capture objective clues for clickbait detection. Extensive experimental re- sults on two real-world datasets show that our proposed MCDM outperforms ofte-of-the-ar approaches for clickbait detection, verifying the effectiveness of integrating subjective and objective preferences for detecting clickbait.
Recently, various clickbait detection models have been developed to identify clickbait articles, including traditional machine learning models and neural language models (Zheng et al., 2018; Glen- ski et al., 2017; Wu et al., 2020; Zhang et al., 2023). They analyze the posts and the corresponding arti- cle content to identify specific linguistic features and patterns, such superlatives, exaggerations, emotive words, and incomplete sentences, typically associated with clickbait (Biyani et al., 2016: Wu et al., 2020). These models achieved desirable de- tection performance where users can avoid wasting time on misleading content, and social media plat- forms can maintain their credibility by providing accurate and informative content.
However, the complex and ever-changing tac- tics used by clickbait publishers bring great chal- lenges to clickbait detection. Previous detection models generally neglect the manipulation inten- tion of clickbait. In fact, it is necessary to ob- tain comprehensive clues about clickbait from both an objective perspective (i.e., the correlation be- tween the corresponding content of articles and posts) and a subjective perspective (i.e., users' po- tential feelings about clickbait). As shown in Fig. I, users impressed by the panic and shock in the post (caused by the terms "carry plastic" and "!") tend to focus on the post due to their subjective prefer- ence. When the users notify "Zimbabwean" and "U.S. dollar" in the article, they recognize the ob- jective topic of the news, i.c., the devaluation of Zimbabwe's currency. Combing the two parts of information, users easily conclude that the post is out of context and exaggerated, showing that com- bining subjective and objective perspectives is nec- essary to capture comprehensive clues of clickbait and potentially improve detection performance. In light of the above discussion, we propose a
1 Introduction
Clickbait often creates inaccurate or misleading posts through the use of exaggeration sensation- alism to manipulate and exploit people's attention and emotions, rather than to inform educate them (Molek-Kozakowske) 2013; Ng and Zhao, 2020). However, these posts often do not match their articles and can even be deceptive, mislead- ing users' judgment of articles and harming the credibility of social media platforms. Accordingly. clickbait detection has become an emerging task for controlling the quality of social media content.
Corresponding author.

---

features from textual content. Most of the cur- rent research is focused on modeling article con- tent and detecting the correlation between posts and articles (Cao et al., 2017; Wei and Wan, 2017; Zheng et al., 2017), with the use of deep learn- ing algorithms (Dong et al., 2018; Anand et al., 2017; Wu et al., 2020; Wei and Nguyen, 2022) in- creasingly employed to enhance the accuracy and efficiency of clickbait detection. Chen et al. (Chen et al., 2015) proposed the use of text and stylis- tic patterns for detection. Potthast al. (Potthast et al., 2016) focused primarily on parts-of-speech features of posts, proposing a machine-learning model based on textual, structural, emotional, top- ical, and image features. Building upon previous work, Chakraborty et al. (Chakraborty et al., 2017) analyzed the social sharing patterns of clickbait and non-clickbait posts and utilized sentiment anal- ysis tools determine the emotional information possessed by each post. Zhou et al. (Zhou, 2017) employed combination of bi-GRU network and self-attention mechanism to learn the post represen- tation posted by users. However, relying solely on post information is insufficient, as longer articles can provide additional valuable information.
SeienA
Helank
bodentd
TIPT
The Nallann
Somer Dagers
Figure An illustration of subjective and objective information obtained from the post and its article. The red part and the blue part show users' subjective per- spectives and objective perspectives, respectively.
multiview clickbait detection model (MCDM) via jointly modeling the subjective and objective prefer- ences embodied in a post and its associated article. Specifically, the model contains two novel com- plementary modules for modeling subjective feel- ing and objective content relevance, respectively. The subjective module adopts BERT (Devlin et al., 2019) to obtain the semantics of post, which cap- tures subjective emotional and writing style fea- tures in user-centric approach. The objective module introduces BERT and Longformer (Belt- agy et al., 2020) to obtain post and article repre- sentations, respectively. It then adopts multi-hean attention to fuse the post and article representations to capture objective clues. Our main contributions are summarized below:
In recent years, more studies have focused on the correlation between posts and articles. Biyani et al. (Biyani et al., 2016) detected the correlation by using the similarity between the title and the first five sentences of the corresponding article. Zheng et al. (Zheng et al., 2017) proposed to recalculate the output of the traditional model according user behavior. Wu al. (Wu et al., 2020) consid- ered the interaction between the context of posts and articles as well as the style patterns of posts. Wei et al. (Wei and Nguyen, 2022) proposed for the first time to use human semantic knowledge in an attention model and used language knowledge graphs to guide the attention mechanism. However, these methods only focus considering the con- tent correlation and neglect emotional and stylistic features that are crucial for clickbait detection. In contrast, our approach detects posts from subjective and objective perspectives jointly, facilitating ac- curate and quick clickbait detection. Furthermore, it can enhance representation by focusing on the objective correlation between posts and articles.
We propose multiview clickbait detection model integrating subjective and objective pref- erences capture comprehensive clues for click- bait detection.
We model news element features in posts towards an objective perspective and apply a multi-head attention mechanism to extract contextual infor- mation in articles, which captures comprehensive clickbait clues in posts.
Extensive experiments on two real-world datasets demonstrate the superior performance of our MCDM over state-of-the-art clickbait detection models, verifying the effectiveness of integrating subjective and objective preferences in detection.
2 Related Work
Clickbait detection has been constantly evolving and improving, from traditional methods based on manual features (Chakrabortyy et al., 2016; Rouvier and Favre, 2016) to automatic feature extraction using deep learning, well as interaction and fu- sion of multiple features (Agrawal, 2016; Zhou, 2017: Zheng et al., 2018). The primary objective clickbait detection to extract crucial linguistic
3 Problem Statement
The clickbait detection task a binary text clas- sification problem. Given a post on social media,

---

maps of all tokens based on context. Following pre- vious research (Sheng al., 2021), subjective feel- ing lexicons are compiled from emotion and writ- ing style knowledge of open source expert knowl- edge bases (Dong and Dong. 2003), matched with words in posts to obtain a collection of subjec- tive feeling vocabulary. Similarly, the open source part-of-speech tagging tool TexSmart (Zhang et al., 2020; Liu et al., 2021) is used to extract entity information in posts and organize the vocabulary of proprietary nouns into collection of objective entity vocabulary in the posts.
Subyertive Prefeuace
Prefereave
Sultunho Pockig
Uigarior Recaciin
CB Tea Lavore
Specifically, in the undirected heterogeneous graph g = (V,E) composed of tokens in post, there are three types of node sets: subjective feel- ings >s, objective entities Vo, and other nodes (not biased towards these two perspectives) VR. We extract the vocabulary node information of the graph, and use BERT to obtain the representations of the three types of vocabulary nodes. We con- catenate them to initialize the heterogeneous graph O H
Figure 2: The architecture of our MCDM model.
represented as P; = [ 102 ..., ma) with rep- resenting the number of tokens, the correspond- ing article is represented as D {di, d2, .... d where m is the number of tokens. From post P;. subjective feelings and objective entity knowledge are extracted to obtain a subjective preference map M M E_ l==1 and an objective preference map M with the ith fraction representing the model's 's degree of attention and preference for to- ken i. The post text P is then used to extract its underlying emotional tendency feature E²", writing style feature S", and initial semantic representa- tion Hº. Subsequently, the post, objective prefer- ence map, and corresponding article D, are fed into the objective content association lcarning module to learn their interaction characteristics on the ob- jective content, resulting in the objective content relation feature Gobj, Finally, the shared feature module is used to obtain the clickbait representa- tion = @u Gobi], which is classified into predefined category (i.e. clickbaitnoon-lickbabt)
yo: H H Then, we initialize the weights of the connected edges between vocabu- lary nodes by cosine similarity, and the correlation
ys H
(0),
(0)
matrix normalized as
H (0)
( 2
+ 2
(1)
12
(0)
112
where H 0)) and H are initial node features, and A (i,j) [0, is the initial weight of the edge connecting the i-th and j-th nodes. By multiplying the normalized degree matrix with the correlation matrix, we define the normalized correlation matrix A for layer as A = (D()) 1 (D(()) y where D is the degree matrix.
(0)
To learn different perspectives of information, we use the heterogeneous dynamic GCN (Linmei et al., 2019; Ye et al., 2020). Specifically, we update the nodes of each layer with a dynamic correlation matrix and expect the weights of the final edges reflect the preference of context nodes for different perspectives. For each layer of GCN, we use the correlation matrix and the state update weight matrix W to update the value of the vocabulary node. Assuming the original input feature vector is O)), the (1 1)-th layer of GCN is
4 Framework
In this section, we introduce the framework of our proposed MCDM is shown in Fig. 2. It consists of four modules, i..,, a preference graph layer, a subjective feeling perspective modeling, an objec- tive perspective content relevance modeling, and a perspectives fusion and output Layer.
4.1 Preference Graph Layer
H 1(+1)= = = 1 W!!)
(2)
This module extracts tokens that biased toward subjective and objective perspectives respectively, generates preference scores for other tokens based on preference, and then dynamically adjusts the
TET
where A is a submatrix of the [-th correlation matrix, whose rows contain all nodes, and columns

---

important clue for detecting clickbait, According the study of mining the quality of social news posts (Yang et al., 2019; Zhu et al., 2022), we extract 8 statistical features of writing styles, including read- ability, logic, credibility, formality, interactiveness, interestingness, user sensation, and integrity.
Figure 3:
The architecture diagram of the SFP module.
We extract subjective perception features from three aspects: semantic information, emotional ten- dency, and writing style. At the semantic level, we utilize the subjective preference map as attention weights to focus on vocabulary that is subjectively favored in the post. This approach generates post- semantic representation H that combines with the subjective preference as
record their correlation with nodes of type E (VS, Vo, )R), W, c RRd is the weight matrix of type in this layer, d' the dimension of the output feature, (-) the activation function, here using Lea to alleviate the problem of gradient disappearance in multi-layer networks. And the node correlation matrix A is updated as
A/ oHH (H ( ((1)))
(3)
H
S
Σ M H
(5)
1011 = a0 + +(1 a)AA
(4)
By using emotional signals for modeling, five emo- tional signals extracted from the previous post are combined to obtain an overall emotional embed- ding vector W and then the emotional embedding vector F.p = oV W E + bg) is learned through a fully connected layer, obtaining the emotional fea- ture representation EP. where V1 is parameter matrix updated during network training, and by is bias term, (-) is the activation function ReLU. Similarly, from the perspective of writing style, we first integrate the previously extracted writ- ing style features in eight aspects into the style embedding vector w = {s,,..., $[5s}, and then use a fully connected layer with ReLU activa- tion function to learn the style embedding vector S LU + bs), obtaining the post style feature representation S
where w(++1); is the learnable weight matrix, a is the sigmoid function, and a is a weighting factor in [0.1]. We obtain the correlation matrix ( finally.
To obtain the subjective preference distribution ssub for each token, we calculate the sum of the normalized correlation between the current node and all non-objective entity nodes. Similarly, the objective preference distribution Mobj is obtained following the same procedure.
4.2 Subjective Feeling Perspective Modeling
As shown in Fig. 3, this module excavates the sub- jectivity of posts from multiple perspectives such writing style and emotional tendency.
Clickbait often contains emotionally or provoca- tively charged information. To comprehensively describe the emotional information contained in posts, this module extracts various emotional fea- tures (Zhang et al., 2021; Jiang et al., 2022), in- cluding emotion categories, emotion vocabulary, emotion intensity, emotion score, and other supple- mentary information, totaling 5 types of features, from post content. addition to the information extracted from the emotional lexicon, a set of pat- tern information that captures non-lexical elements is introduced, including punctuation marks, emoti- cons, and writing habits description. For example, capital letters are used in a post to express stronger emotions, while emoticons such as ":)" can also replace vocabulary express various emotions.
To comprehensively represent the subjective fea- tures of a post, the features from three perspectives of the post P are fused. Specifically, the vector obtained by concatenating the semantic, emotional, and stylistic features the post [HS ".,,E,'"" is linearly transformed and mapped to a Query vec- tor matrix Q, a Key vector matrix K, and Value vector matrix V respectively. Then, multi-head attention mechanism is used to model and compute the weights of each element to be attended among multiple elements. Finally, the output vectors of all attention heads are concatenated to obtain the multi multi-persppectve features Guub that compre- hensively represent the subjective feeling.
4.3 Objective Content Relevance Modeling
Meanwhile, special writing styles are also its typical means of expression, which distinguishes the good and bad quality of news and serves as an
This module focuses on the keywords related to the news elements, aiming to capture the correlation

---

where HD E RR and the function Flatten(-)
flattens Af HH' into a vector.
We use a collaborative attention network to cap- ture the content-related relationship between posts and articles, where different attention distributions represent different degree of clickbait of articles for posts, thereby ensuring their deep interaction:
Figure 4: The architecture of the OPCR module.
between the posts and the corresponding article content from an objective perspective. The details of the module are shown in Fig. 4:
(9) V HP, and dg 2d the size of the BiLSTM hidden units. To enhance network ability, the multi-head atten- tion uses different linear projections of the query Q. key K and value V. projecting them h2 times, and then performing parallel scaled dot- dot-product atten- tion. Finally, the processed results are concatenated and projected to obtain new representation:
H = Attention Q K, V)
where the matrix Q H.P
Through BiLSTM, the imitial semantic represen- tation H. of the post is obtained, and the hidden state hf R22d is calculated as hy [h,, hi] where h; and h; are the hidden states in the forward and backward passes of BiLSTM, respectively. d is the initial embedding dimension of the post.
To create an attention representation combin- ing the objective entity features of the post, the weighted average is calculated based on the word- level scores of the post's objective entity map. The post representation H. that focuses on objective content is obtained by the average pooling layer after the BiLSTM, which is expressed as
Gobj Multihead (Q, K, V)
(10)
where the global attention of content-relatedness applies h2 different heads, and w E 2dxx,
w E 222xd, w. E R2dxd_ wo E Rddx2d
are trainable parameters, with dj = 2d Finally, we obtain the content-related interaction features Gobj that contain objective entity information.
HP-
h
M,0j
(6)
n
4.4 Perspectives Fusion and Output Layer
We use Longformer to encode the article D {dl, dm. to obtain the initial semantic repre- sentation of the article, denoted as D. and further encode it using BiLSTM to capture the content fea- matrix 1D hh: ...; hnl Rmm2dd We then guide the post to focus on the keywords in the article to establish a connection between the article and the post's 's objective entity information.
We combine the representation of subjective multi- perspective posts and objective content relevance to obtain the final clickbait representation (ub, Gobj], and the classification category 0 MLP(c) is calculated. Then, we train our model by minimizing the cross- loss L. 9)- Besides, we use the cosine similarity between subjective sentiment and objective entity prefer- ence maps, to guide the classification to be more closely aligned with specific subjective and objec- tive perspectives, auxiliary loss is introduced:
tures of the post. The article is modeled
First, we replicate the post content represen- tation vector H m times and create matrix H'P E Rdmx2d Second, the overall semantics of the article is usually obtained by its components collectively. Therefore, mechanism for focusing on important words in the article by extending the vector U2 to the matrix W2 E x as follows:
M
Mobj
Csim(Mau (Msub Mobjy
(11)
propose an attention
Moby
1/2
In addition, we flip the actual labels and exchange the subjective and objective preference maps input, introducing a signal of reverse supervision to em- phasize the current preference result. Accordingly, another auxiliary loss is employed:
A = Softmax(tanh((( (H' -W)) W2) (7)
where A₁ E Rmxhi hi is the number of attention heads, and each column of Ai is normalized by Softmax. After computing A1, the objective con- tent representation of the article HD is obtained:
Lete(yoom g') CELoss(Mem, g')
(12)
where the flipped label Yoon =| y J. and the predicted result y' MLP(c') where e' is the pref- erence maps with the opposite viewpoints used as
HD_ = Flatten AT H)
(8)

---

output. We obtain the final objective function:
KED (Wei and Nguyen, 2022): A method of inte- grating human semantic knowledge into a neural network model, by building language knowledge graph to guide attention mechanisms for inference. Implementation Details: In the experiments, we used Bert to initialize the word embedding layer, each with a dimension of 768 dimensions and Long- former to initialize the article embedding. The GCN network was initialized with glorot, while the classification layer of BERT was initialized with Xavier normal. The learning rate was set to 0,0001, and the size of each mini-batch is 8. The maxi- mum sequence length for posts is 100, and 600 for articles. We conducted 20 iterations and set the output unit dimension of BiLSTM 128. In the SFP module, we utilized shared learning of the multi-head attention mechanism with 3 heads. To avoid overfitting, we set the dropout rate to 0.1. For the OPC module, we set the number of attention heads for learning the content of articles to 5 and for global content-related attention to 2. Addition- ally, we used AdamW as our optimizer, and the weight decay value for I.2 regularization was set to 0.0001. These key parameters were tuned on the validation set. We use four metrics, i.e., accuracy, precision, recall, and Fl-score, for evaluation.
L = Lis + ai sii + ar2 (yean: f')
(13)
where on and a2 are weighting factors that measure the degree of involvement of map similarity and flipped labels. Since the overall loss function is dominated by the final classification loss of the model, their values range is in [0.1, 0.5].
5 Experiments and Analysis
5.1 Experimental Setup
Datasets: In our experiment, we utilized two real- world datasets, namely Clickbait Challenge17 and FNC Challenge". Both datasets contain posts and articles. The Clickbait Challenge17 7 dataset com- prises 33,000 samples collected from 27 major news agencies in the United States. Similar to (Dong et al., 20,8), we labeled each pair of posts and article samples with a clickbait tag if their av- erage score was above 0.5. The FNC Challenge dataset consists of 89,000 samples. Following pre- vious studies (Dong et al., 2018; Wu et al., 2020), we classify "unrelated" items as clickbait. Methods: We compared MCDM to nine SOTA baselines and divided the baselines into two groups: Using only posts: DSSM (Huang et al., 2013): A deep network is used to obtain hidden features of the input and quantify the similarity in the po- tential representation space. CLSM (Shen et al., 2014): A variant of DSSM, uses convolutional neu- ral networks to extract intrinsic semantic features. CBCNN (Zheng et al., 2018): A clickbait detection model based on texICNN (Chen, 2015).
5.2 Performance Evaluation
Table presents the overall performance of all the comparison methods. According to the results, we have several main findings.
1) The methods that incorporate article and post information (BiGRU-Att, SATC, KED) outperform those that solely rely on post information (DSSM, CLSM, CBCNN), emphasizing the importance of jointly detecting post and article information.
Using both posts and articles: LiNN (Glenski et al., 2017): A model using LSTM and CNN net- works learn the vectorized text and visual con- tent, respectively. MSA (Kumar et al., 2018): The attention- based bidirectional RNN method used to learn the input data, and then the latent informa- tion and relational knowledge are combined into the Siamese network. BiGRU-Att (Zhou, 201 7): Attention-based BiGRU model that focuses on la- tent features contained in the semantics of post and its linked articles. LSDA (Dong et al., 2018): The model uses GRU-Att to measure global and local similarities between post and article represen- tation vectors. SATC (Wu ct al., 2020): A detection method that is sensitive to post style and consid- ers synergistic attention between posts and articles.
2) The methods that utilize attention to capture the interaction between articles and posts (e.g., BiGRU-AIt, LSDA) perform better than those with- out attention (e.g., CBCNN, LiNN). This is because the attention mechanism highlighting important contexts in both posts and articles allows for better modeling of their association in clickbait detection. 3) The methods that consider clickbait emotional and stylistic patterns (SATC, KED) show superior performance over most other methods, demonstrat- ing their strong ability to learn post representations and capture subjective sentiments.
4) Our MCDM performs better compared to the baselines. We attribute its superiority to three ad- vantages: (1) through the update of the preference
https://webbs. http:///wwbeeeeeecocnecccchhhaae

---

Table 1: Performance of MCDM and basclines on datasets.
Clickbail Challenge17 17
FNC Challenge
Methods
Accuracy
Precision
Recall
Accuracy
Precision
Recall
F1
DSSM (Huang et al., 2013)
0.817
0.655
0.661
0.658
0.747
0.894
0.740
0.811
CLSM (Shen et al., 2014)
0.833
0.683
0.643
0.662
0.756
0.959
0.762
0.853
CBCNN (Zheng al., 2018)
0.844
0.654
0.653
0.653
0.789
0.852
0.845
0.857
LiNN (Glenski ct al., 2017)
0.827
0.642
0.621
0.631
0.868
0.925
0.884
0.913
MSA (Kumar et al., 2018)
0.826
0.699
0.474
0.565
0.859
0.920
0.884
0.913
BiGRU-Att (Zhou, 2017)
0.856
0.719
0.650
0,683
0,879
0.924
0.897
0.919
LSDA (Dong et al., 2018)
0.860
0.722
0.699
0.710
0.894
0.933
0,912
0.918
SATC (Wu et al., 2020)
0.869
0.762
0.712
0.737
0.907
0,929
0.903
0.916
KED (Wei and Nguyen, 2022)
0.873
0.761
0.745
0.753
0.918
0.927
0,913
0.925
SFP
0.865
0.787
0.745
0.766
0.916
0,936
0.918
0.927
OPCR
0.873
0.776
0.749
0.764
0.922
0.941
0.922
0.936
MCDM
0.881
0.782
0.754
0.768
0.927
0.958
0.922
0.945
Table 2: Performance comparison of MCDM variants.
variants in both datasets, indicating the effective- ness of the subjective perception features selected in this study. The SFPP-pstt variant has the worst performance, suggesting that not delving into other features of posts cannot enhance the experimental results. The SFP-submap has the best performance among several variants, confirming the importance of capturing semantic and writing style features comprehensively characterizing subjective prefer- ence, and learning subjective preference in posts optimizes the detection of clickbait.
Clickbait17
FNC
MCDM variants
Acc.
Acc.
F1
SFP post
0.836
0.751
0.891
0.896
SFP- submap
0.854
0.759
0.909
0.919
SFP- emotion
0.858
0.755
0.882
0.893
SFP style
0.853
0.752
0.901
0.918
SFP
0.865
0.766
0.916
0.927
OPCR post
0.842
0.740
0.903
0.927
OPCR objpost
0.853
0.755
0.897
0.913
OPCR & a
0.862
0.768
0.916
0.929
OPCR
0.873
0.764
0.922
0.936
In the ablation experiment, the overall perfor- mance of the OPCR model higher than that the other variants, indicating the effectiveness the feature combinations selected in this study. In both datasets, the experimental effects of OPCR- post variant and OPCR-objpost variant are not good as that of the OPCR- post & article variant, which means that the effect of only using post fea- tures is inferior to that of using both post and ar- ticle features, demonstrating the effectiveness of using post and article information simultancously. and validating that the use of more relevant text features improves the detection of clickbait. There- fore, each module proposed in the SFP and OPCR models plays a critical role in improving the overall performance of the models.
graph network for posts, it can obtain more com- prehensive subjective and objective feature repre- sentations of post content; (2) the introduction of subjective sentiment features allows for capturing potentially clickbait textual cues from subjective perspective; (3) combining news entity preference features improves the model's ability to handle sce- narios with unclear subjective sentiments and fo- cuses on the interaction between posts and articles, allowing for better modeling of their joint represen- tations from an objective perspective.
5.3 Ablation Studies
In this section, conduct ablation studies on the SFP model by investigating the usage of initial post semanties, subjective preference features, emo- tional features, and writing style features, as well as on the OPCR model by exploring post-article encoding, objective entity preference learning, and content correlation learning modules, to demon- strate the effectiveness of each module's design.
5,4 Qualitative Analysis
We analyze the impact of post length on perfor- mance. The experimental results, shown in Fig. 5, indicate significant improvement when the post length increases from 30 100 words Although the Fl-score is slightly higher for the 150-word post, the overall improvement not substantial. The threshold parameter n is set to 100 for the Clickbait Challenge17 7 dataset and 150 for the FNC
The experimental results, as shown in Table 2, reveal that in the subjective perception module ab- lation experiment, SFP performs better than several

---

Troeatt Searce SWSt Uneccsoat ain
3.45
-
Cidhas ARC
Posdictio
-X-
C19004 FL.
-8" PRC AGS.
-4-
Predictia OPCS Noddicher CM0 C00 Ne-diohery
Figure 6: Three examples of clickbait and non non-clickbaii posts. The red markers indicate the vocabulary of sub- jective feelings, while the blue markers indicate the vocabulary of objective entities. The darker the color, the higher the preference for that particular word.
3.00
3.75
25
100
125
152
The length
Figure 5: Performance of the MCDM model under dif- ferent values of the parameter n.
bait and non-clickbait posts. The MCDM model's overall success rate in classification, combining both subjective and objective perspectives, is an- alyzed. Fig. 6 presents three posts successfully classified by the MCDM, SFP, and OPCR models, demonstrating the avoidance of biases commonly observed in clickbait detection through the fusion of subjective and objective features.
Challenge dataset, considering the character limit of 140 words in Twitter for the former dataset. Tak- ing into account time and memory constraints, we choose 100 the post length.
We further investigate the effectiveness of 01 and 02 balancing the contribution of preference map loss and inversion label loss. Table shows the experimental results. Increasing a (larger than 0.3) harms MCDM's effectiveness, while increas- ing 02 significantly enhances MCDM's overall per- formance. The optimal performance is achieved 01 0.3 and a2 0.5. As 02 increases, the weight of the inversion label loss in the total loss rises, allowing MCDM focus more on distin- guishing preferences opposite to its own. This distinction between subjective and objective per- spectives improves overall performance. By ap- propriately weighing different perspectives in the loss function, controlled by 01,, MCDM can better adjust to different preference maps, enhancing its performance and generalization ability.
In the first case, the post's 's sentiment was weak. leading the SFP model to misclassify it non- clickbait, while the OPCR model correctly identi- fied its lack of actual relationship with the article and categorized as clickbait. the second case, the post exhibited strong emotional bias and writing style signals, accurately recognized by the subjec- tive preference SFP model. However, the OPCR model mistakenly classified it as non-elickbait due to the presence of content appearing in the arti- cle. In the third post, the OPCR model effectively detected and correctly classified the extensive ob- jective entity information. However, the SFP model erroncously classified as clickbait due to the pres- ence of emoticons and emotionally biased adjec- tives. These cases highlight MCDM's efficacy in incorporating both subjective and objective feature associations for clickbait detection.
Table 3: Detection accuracy of MCDM on Clickbait Challenge17 17 dataset with different in and a2-
Q1
0.1
0.2
0.3
0.4
0.5
7
Conclusion
0.722
0.858
0.859
0.864
0.871
0.2
0.841
0.869
0.867
0.880
0.876
In this paper, we propose MCDM, a clickbait detec- tion framework guided by subjective and objective perspectives. MCDM takes into account the emo- tional and stylistic characteristics of posts from the subjective viewpoint and the clickbait information from the objective perspective by combining both post and article content. Extensive experiments two real-world datasets demonstrate that MCDM effectively improves the detection performance of clickbait by combining the features of posts from the subjective level and the interaction between posts and articles on the objective entity level.
02
0.3
0.874
0.875
0.877
0.879
0.881
0.4
0.864
0.866
0.871
0.824
0.877
0.5
0.862
0.860
0.868
0.873
0.876
6
Case Studies
We evaluate the model's classification accuracy by extracting subjective and objective preference fea- tures. The SFP model and the OPCR model are employed to map attention scores for subjective and objective perspectives, respectively, on click-

---

Limitations
social media? understanding the production and con- sumption of clickbaits in twitter. Proceedings of the ACM on Human-Compple Interaction, (CSSCW): 21.
Although extensive experiments have demonstrated that MCDM can effectively improve the perfor- mance of the model, mentioned above, there are limitations to consider. Firstly, both perspec- tives' prior information relies on manually de- signed features, whereas an automated detection process based on text content, independent of lexi- cons and tools, is desired. Additionally, our current model only focuses on clickbait detection based on textual information, neglecting the rich subjec- tive and objective information conveyed through images accompanying the posts. Thus, our model lacks analysis of image content. This will also be direction of improvement for our future work.
Yahui Chen. 2015. Convolutional neural network for sentence classification. Master's thesis, University of Waterloo, Waterloo City.
Yimin Chen, Niall J Conroy, and Victoria Rubin. 2015. Misleading online content: recognizing clickbait as" false news". In Proceedings of the 2015 ACM on workshop on multimodal deception detection, pages 15-19.
Jacob Devlin, Ming- Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: pre-training of deep bidirectional transformers for language under- standing. In NAACL-HLT (1), pages 4171-4186. As- sociation for Computational Linguistics.
Acknowledgements
Manqing Dong, Lina Yao, Xianzhi Wang. Boualem Benatallah, and Chaoran Huang. 2018. Similarity- aware deep attentive model for clickbait detection. In Advances in Knowledge Discovery and Data Mining, pages 56-69 36-69. Springer International Publishing.
This work is supported by National Natural Science Foundation of China (No. 62102026).
References
Zhendong Dong and Qiang Dong. 2003. Hownet-a hybrid language and knowledge resource. In Inter- national conference on natural language process- ing and knowledge engineering, 2003. Proceedings. 2003, pages 620-824. IEEE
Amol Agrawal. 2016. Clickbait detection using deep learning. In 2016 2nd international conference on next generation computing technologies (NGCT), pages 268-272. IEEE.
Maria Glenski, Ellyn Ayton, Dustin Arendt, and Svit- lana Volkova. 2017. Fishing for clickbaits in social images and texts with inguistically-infuss neural network models. arXiv preprint arXiv: 1710:00710060
Ankesh Anand, Tanmoy Chakraborty, and Noseong Park. 2017. We used neural networks to detect click- baits: You won't believe what happened next! In Advances in Information Retrieval: 39th European Conference on IR Research, ECIR 2017, Aberdeen, UK, April 8-13, 2017, Proceedingy 39, pages 541- 547. Springer.
Po-Sen Huang. Xiaodong He, Jianfeng Gao, Li Deng. Alex Acero, and Larry Heck. 2013. Learning deep structured semantic models for web search using clickthrough data. In Proceedings of the 22nd ACM international conference on Information & Knowl- edge Munagement, pages 2333-2333
Iz Beltagy, Matthew E. Peters, and Arman Cohan, 2020. Longformer: The long-document transformer. CoRR, abs/2004.051.50
Prakhar Biyani, Kostas Tsioutsiouliklis, and John Black- mer. 2016. amazing secrets for getting more clicks": Detecting clickbaits in news streams using article informality. In Proceedings of the AAAI Con- ference Artificial Intelligence, volume 30.
Xinyu Jiang, Qi Zhang, and Chongyang Shi. 2022. Hi- erarchical neural network with bidirectional selection mechanism for sentiment analysis. In IJCNN, pages 1-8. IEEE.
Vaibhav Kumar, Dhruv Khattar, Siddhartha Gairola, Yash Kumar Lal, and Vasudeva Varma. 2018. Iden- tifying clickbait: A multi-strategy approach using neural networks. The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pages 1225-1222
Xinyue Cao, Thai Le, et al. 2017. Machine learning based detection of clickbait posts in social media. arXiv preprint arxiv:1710.01997
Abhijnan Chakraborty, Bhargavi Paranjape, Sourya Kakarla, and Niloy Ganguly. 2016. Stop clickbait: Detecting and preventing clickbaits in online news media. In 2016 JEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), pages 9-16. IEBE.
Hu Linmei, Tianchi Yang, Chuan Shi, Houye Ji, and Xiaoli Li. 2019. Heterogeneous graph attention net- works for supervieee short text classification. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing TEMNLP-IJCNLP. pages 4821 4821-4830.
Abhijnan Chakraborty, Rajdeep Sarkar, Ayushi Mrigen, and Niloy Ganguly. 2017. Tabloids in the era of

---

Lemao Liu, Haisong Zhang, Haiyun Jiang, Yangming Li, Enbo Zhao, Kun Xu, Linfeng Song, Suncong Zheng. Botong Zhou, Dick Zhu, et al. 2021. Texs- mart: A system for enhanced natural language under- standing. In Proceedings of the 59th Annual Meet- ing of the Association for Computational Linguistics and the 1 11th International Joint Conference on Nat- ural Language Processing: System Demonstratioss, pages 1-10.
Yuting Yang. Juan Cao. Mingyan Lu, Jintao Li, and Chia- Wen Lin. 2019. How to write high-quality news social network? predicting news quality by mining writing style. arXiv preprint arXiv:/902.00 1902.00750.
Jin Ye, Junjun He, Xiaojiang Peng, Wenhao Wu, and Yu Qiao. 2020. Attention-driven dynamic graph con- volutional network for multi-lahel image recognition. In Computer EssionEECVv 2020: 16th European Conference, Glasgow, UK, Augast 23-28, 2020, Pro- ceedings, Part XXI 16, pages 649 665. Springer.
Katarzyna
2013. Towards a pragma- linguissti framework for the study of sen- sationalism in news headlines. Discourse & Commu- nication, 7(2):173-197
Haisong Zhang. Lemao Liu, Haiyun Jiang, Yangming Li, Enbo Zhao, Kun Xu, Linfeng Song, Suncong Zheng, Botong Zhou, Jianchen Zhu, et al. 2020. Texs- mart: A text understanding system for fine-grained ner and enhanced semantic analysis. arXiv preprint 2oXiv:2012.15633
Yu-Leung Ng and Xinshu Zhao. 2020. The human alarm system for sensational news, online news head- lines, and associated generic digital footprints: A uses and gratifications approach. Communication Research, 47(2):251-274
Qi Zhang. Yayi Yang, Chongyang Shi, An Lao, Liang Hu, Shoujin Wang, and Usman Naseem. 2023. Ru- mor detection with hierarchical representation on bi- partite ad hoc event trees. IEEE Transactions on Neural Networks and Learning Systems.
Martin Potthast. Sebastian Köpsel, Benno Stein, and Matthias Hagen. 2016. Clickbait detection. In Ad- vances in Information Retrieval: 38th European Con- ference on IR Research, ECIR 2016, Padua, Italy, March 20-23, 2016. Proceedings 38, pages 810-817. Springer.
Xueyao Zhang, Juan Cao, Xirong Li, Qiang Sheng, Lei Zhong, and Kai Shu. 2021. Mining dual emotion for fake news detection. In Proceedings of the web conference 2021, pages 3465-3476.
Mickael Rouvier and Benoit Favre 2016. Sensei-lif at semeval-2016 task 4: Polarity embedding fusion for robust sentiment analysis. In Proceedings of the 10th International Workshop on Semantic Evaluation, ComEEvalNAAALL HLT 2016, pages 202-208.
Hai-Tao Zheng. Jin- Yuan Chen, Xin Yao, Arun Kumar Sangaiah, Yong Jiang, and Cong-Zhi Zhao. 2018. Clickbait convolutional neural network. Symmetry, 10(5):138-149,
Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, and Grégoire Mesnil. 2014. A latent semantic model with convolutional poolingg structure for information retrieval. In Proceedings of the 23rd ACM interna- tional conference on conference on information and knowledge management, pages 101-110.
Hai-Tao Zheng, Xin Yao, Yong Jiang, Shu-Tao Xia, and Xi Xiao. 2017. Boost clickbait detection based on user behavior analysis. In Web and Big Data: First International Joint Conference, APWeb- 2017, Beijing, China, July 7-9, 2017, Proceedings, Part II 1, pages 73 80. Springer.
Qiang Sheng. Xueyao Zhang, Juan Cao, and Lei Zhong. 2021. Integrating pattern-and fact-based fake news detection via model preference learning. In Proceed- ings of the 30th ACM international conference on information & knowledge management, pages 1640- 1650.
Yiwei 7hou. 2017. Clickbait detection in tweets using self-attentive network. arXiv preprint 1rX1v:1710005364.
Yongchun Zhu, Qiang Sheng. Juan Cao. Qiong Nan, Kai Shu, Minghui Wu, Jindong Wang, and Fuzhen Zhuang. 2022. Memory-guided multi-view multi- domain fake news detection. IEEE Transactions on Knowledge and Data Engineering.
Feng Wei and Uyen Trang Nguyen. 2022. An attention- based neural network using human semantic knowl- edge and its application to clickbait detection. IEFF Open Journal of the Computer Society, 3:217-232.
Wei Wei and Xiaojun Wan. 2017. Learning to identify ambiguous and misleading news headlines. In Pro- ceedings of the 26 International Joint Conference on Artificial Intelligence, IJCAI, pages 4172-4178.
Chuhan Wu, Fangzhao Wu, Tao Qi. and Yongfeng Huang. 2020. Clickbait detection with style-aware title modeling and co-attention. In Chinese Computa- tional Linguistics- 19th China National Conference, CCL 2020. Proceedings, volume 12522 of Lecture Notes in Computer Science, pages 430-443. Springer.


Reviews:
[{"review_id": "TxAXtKrVDu", "reviewer": "Reviewer_mx4P", "summary": "", "questions": "A . The second contribution presented in the paper confuses the reader, as the relevance of titles to articles has become a widespread concern in many past studies. Therefore, it may be difficult for readers to understand exactly what is the innovation of this paper in this respect.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate you taking the time and providing valuable comments. In the following, we provide a detailed response to address all of your concerns.\n\n**1. Regarding the motivation and purpose of the research and the framework of the article**\n\n+ It is essential to recognize that clickbait often utilizes inaccurate or misleading tactics to manipulate and exploit people's attention and emotions, thus distorting their perception of information and undermining the credibility of social media platforms. As a result, clickbait detection has emerged as a crucial and practical task for maintaining the quality of social media content.\n\n    In our research, we are motivated by the realization that achieving a comprehensive clickbait detection requires an understanding of both the emotional and stylistic elements (subjective) as well as the relevance of the objective content (objective). Our objective is to develop a more reliable approach for automatically detecting clickbait by integrating both subjective and objective perspectives. By doing so, we aim to enhance the effectiveness of clickbait detection and mitigate its negative impact on users and social media platforms.\n\n+  Regarding the framework of our article, we will enhance the paragraph preceding the contribution summarization in the introduction and provide a more comprehensive introduction of the framework. Specifically, we will\n    - highlight how the subjective and objective perspectives are integrated to enhance the accuracy of clickbait detection,\n    - supplement more details of each module in our framework to show how we construct the network architecture in the framework.\n\nWe believe that these revisions will address the shortcomings you mentioned and provide a more comprehensive and structured introduction.\n\n\n**2. Regarding baseline method**\n\nThank you for your valuable comments. In this work, we initially chose representative baselines of each category for the clickbait detection evaluation to guarantee a comprehensive comparison between our proposed MCDM with existing clickbait detection models. In addition, we compared our MCDM with the state-of-the-art baseline (KED [6] published at the end of 2022 which is the current state-of-the-art in the field and served as our latest baseline) to show the state-of-the-art performance of MCDM and verify the superiority of our proposed model.\n\nFurthermore, we have analyzed some recent literature, i.e., LSACD [14], RCD [10], and CVM [12], and compared our MCDM with the three baselines during rebuttal. We report their performance on the two datasets in the following table where ∗ indicates the statistically significant improvement (i.e., two-sided t-test with p < 0.05). We will complete the experiments and include the corresponding results in the final version.\n\n| | |Clickbait |Challenge17  | | | FNC | Challenge | |\n|:---|:---|:---|:---|:---|:---|:---|:---|:---|\n| Methods| Accuracy| Precision| Recall| F1|Accuracy| Precision| Recall| F1|\n|LSACD [14]| 0.846 | 0.742 | 0.698 | 0.797 | 0.913 | 0.836 | 0.847 | 0.825|\n|RCD [10]| 0.857 | 0.769 | 0.756 | 0.791 | 0.862 | 0.772 | 0.824 | 0.743|\n|CVM [12]| 0.860 | - | - | 0.694 | - | - | - | -|\n|MCDM(ours) | **0.881*** | **0.782*** | **0.754*** | **0.768*** | **0.927*** | **0.958*** | **0.922*** | **0.945*** |\n\nTo address your concerns, we will discuss baselines [10,12,14] in the related work and include the baselines in the experiments.\n\n**3. Regarding performance improvement** \n\nFirstly, we want to emphasize that our datasets are collected from real-world scenarios, which often result in uneven data distribution. As such, in the context of this classification task, our focus extends beyond merely improving accuracy; it includes placing substantial importance on the enhancement of the F1 score. This metric is particularly significant for addressing imbalanced data and capturing the trade-off between precision and recall, which is a common characteristic of clickbait detection.\n\nIn Table 1, we compared the baselines with our proposed MCDM and its two variants (SFP and OPCR):\n+ The Subjective Feelings Preference-based Clickbait Detection Model (SFP) leverages subjective sentiment cues such as emotional tendencies and writing style to identify posts with inducing (clickbait) characteristics.\n+ The Objective Content Relationship-based Clickbait Detection Model (OPCR) utilizes entity information related to the post's news elements and combines it with corresponding article content.\n+ The Model Combining Subjective and Objective Perspectives (MCDM) integrates both subjective and objective viewpoints.\n\nFrom Table 1, we can observe that MCDM outperforms almost all baselines (including the state-of-the-art baseline KED) on both two datasets in terms of all four metrics (accuracy, precision, recall, and F1 score). For example, we show the corresponding results of our MCDM and KED in the following table (copied from Table 1). Herein, we highlight the **statistically significant improvement** (i.e., **two-sided t-test with p < 0.05**) indicated by $*$. These impressive results show significant advantages of our proposed MCDM over KED. Also, MCDM achieved more substantial improvement over other clickbait detection models. Our MCDM's advantages stem from its incorporation of three main features: comprehensive subjective and objective content representations, subjective sentiment features, and news entity preference feature that enhance the model's ability to handle scenarios with unclear subjective sentiments. \n\n| | |Clickbait |Challenge17  | | | FNC | Challenge | |\n|:---|:---|:---|:---|:---|:---|:---|:---|:---|\n| Methods| Accuracy| Precision| Recall| F1|Accuracy| Precision| Recall| F1|\n|KED | 0.873 | 0.761 | 0.745 | 0.753 | 0.918 | 0.927 | 0.913 | 0.925|\n|MCDM(ours) | **0.881*** | **0.782*** | **0.754*** | **0.768*** | **0.927*** | **0.958*** | **0.922*** | **0.945*** |\n\nIn summary, our model's performance improvement over the baselines is multi-faceted and comprehensive, indicating the significant superiority of MCDM over the baselines.\n\n\n**4. Regarding outdated references**\n\nTo address your concerns about the outdated references, we would like to provide a comprehensive analysis of recent literature, and we will add more recent references in the final version.\n\n+ Since our paper was submitted on June 16, 2023, there were only three articles related to clickbait detection listed in 2023:\n    - Reference [1] focuses on the study involving a Turkish language dataset.\n    - Reference [2] primarily contributes to addressing clickbait editing decisions within the competitive context of platform regulation instead of clickbait click models.\n    - Reference [3] explores detecting clickbait by utilizing ChatGPT's response to questions\n    The three models are unsuitable for experimental comparison.\n\n+ In the landscape of 2022 publications related to clickbait detection, only nine relevant works were formally published. Some of these studies can be summarized as follows, and others even omitted critical dataset and task details, making it challenging to perform a comprehensive comparison:\n    - Reference [4] approaches clickbait from the perspective of blockchain research, focusing primarily on evaluating detection performance from security and memory angles.\n    - Reference [5] employs a Chinese dataset and integrates semantic, syntactic, and auxiliary information for clickbait detection.\n\n+ Note that there have not been quite a few studies on clickbait detection recently. The reason for the relatively limited number of recent references could be as follows:\n    - This research task is relatively niche, being a subfield of fake news detection, but it holds significant real-world importance in purifying the atmosphere of social networks. Much of the research in this area was conducted in 2017 and 2018 after the release of the Clickbait Challenge 17 competition and dataset.\n    - In recent years, the NLP field has attracted more attention to Natural Language Generation (NLG), and there might be relatively fewer studies on classification tasks and even clickbait detection.\n\nWe have taken your suggestion seriously and conducted in-depth analyses of valuable recent literature [7,8,10,12,14]. We will incorporate references to works published after 2021 [1,2,3,7,8,10,12,14] in the Related Work section to make our study more comprehensive and persuasive.\n\n\n**Regarding the second contribution**\n\nThank you for your comments. Due to space constraints, the articulation of our contributions might have led to some confusion. We apologize for any ambiguity that may have arisen from our previous description. To address this, we have revised and reorganized our wording to provide you with a clearer and more comprehensive explanation of the second contribution:\n+ Our method stems from the foundation of examining objective entity elements within posts. By harnessing the inherent attributes of news entity elements, which posts tend to embrace from an objective perspective, and by synergizing this with contextual information from the associated articles, we aim to more precisely capture the implicit triggering cues embedded within posts. This approach is meticulously designed to circumvent misinterpretations stemming from subjective biases.\n+ This innovation aligns seamlessly with our motivation to explore the dual facets of subjective and objective perspectives. Specifically, we leverage extracted entity elements as auxiliary information for posts, incorporating a dynamic Graph Convolutional Network (GCN) to capture and underscore the objective dimensions of post features related to news entities. By harnessing the entity information correlated to the news elements in posts and incorporating corresponding article content, our model adopts a human-like in-depth analysis of the objective perspective of clickbait content. We extract features associated with the objective entity content relevance and create comprehensive representations of both the post and its corresponding article. This approach stands in stark contrast to previous studies, distinguishing our work and methodology from existing literature.\n\nWe hope that our elaboration clarifies the uniqueness and technical significance of our second contribution. We will polish the statement of the second contribution accordingly in the final version.\n\n[1] Artificial intelligence on the advance to enhance educational assessment: Scientific clickbait or genuine gamechanger?. Journal of Computer Assisted Learning, 2023.\n\n[2] A game theory framework of the relation between journalism, users, and platforms. New Media & Society, 2023.\n\n[3] Clickbait Detection via Large Language Models. arXiv 2023.\n\n[4] Blockchain-enabled deep recurrent neural network model for clickbait detection. IEEE Access 2021.\n\n[5] Clickbait detection on WeChat: A deep model integrating semantic and syntactic information. KBS, 2022.\n\n[6] An Attention-Based Neural Network Using Human Semantic Knowledge and Its Application to Clickbait Detection. IEEE Open Journal of the Computer Society, 2022.\n\n[7] Detecting Clickbait in Chinese Social Media by Prompt Learning. CSCWD 2023.\n\n[8] Clickbait spoiler type identification with transformers. Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023). 2023.\n\n[10] Rumor and clickbait detection by combining information divergence measures and deep learning techniques. Proceedings of the 17th International Conference on Availability, Reliability and Security. 2022: 1-6.\n\n[11] Clickbait detection in tweets using self-attentive network. arXiv 2017.\n\n[12] Clickbait Detection via Contrastive Variational Modelling of Text and Label. IJCAI 2022.\n\n[13] Clickbait Headline Detection in Indonesian News Sites using Multilingual Bidirectional Encoder Representations from Transformers (M-BERT). arXiv 2021.\n\n[14] A deep model based on lure and similarity for adaptive clickbait detection. KBS 2021.\n\n[15] Clickbait detection with style-aware title modeling and co-attention. CCL 2020.\n\n\nThank you for your valuable feedback, which has driven us to refine our presentation and enhance the clarity of our work.", "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": "1. MCDM introduces two new complementary modules to model subjective perception and objective content association, respectively. Compared with the baseline method, MCDM comprehensively considers the cues about headline party and improves the detection performance.", "reasons_to_reject": "1. In the introduction, the description of the motivation and purpose of the research is not comprehensive enough, and there is a lack of a comprehensive elaboration of the framework of the article.\n2. The baseline method compared in the article is somewhat outdated, and there is only one method after 21 years. This situation leads to a lack of sufficient convincing arguments for the validity of the model.\n3. Compared with the baseline method, the model proposed in the paper does not show significant improvement in overall accuracy, especially on the Clickbait Challenge17 dataset. Although the author may have made some innovations and improvements in the model design and experiment process, from the experimental results, these improvements did not significantly improve the performance of the model.\n4. Many outdated references were cited, with only four from 2021 onwards. This also proves that the method proposed in the article is not advanced.", "questions_to_authors": "A . The second contribution presented in the paper confuses the reader, as the relevance of titles to articles has become a widespread concern in many past studies. Therefore, it may be difficult for readers to understand exactly what is the innovation of this paper in this respect.", "paper_topic_and_main_contributions": "This paper proposes a multi-view titled party detection model (MCDM) to address the problem that existing models fail to identify and mine titled party manipulation intentions from the perspective of user agents. MCDM introduces two new complementary modules for modeling subjective perception and objective content relevance, respectively. The subjective sentiment module adopts a user-centered approach to capture the subjective characteristics of posts, such as language patterns and emotional inclusion. The targeting module explores news elements from posts and models article content relevance to capture targeting cues for headline party detection. Extensive experimental results on two real datasets demonstrate that MCDM outperforms the state-of-the-art methods in headline fraud detection.", "excitement": 4, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "None", "Typos_Grammar_Style_and_Presentation_Improvements": "None"}, "extra_text": {}}, {"review_id": "tLBPwxx2cs", "reviewer": "Reviewer_FUSD", "summary": "", "questions": "1) This work aims to address clickbait detection task, and formulates it as a text classification problem. In practice it would be more interesting to explore multimodal data sources such as images for the problem.\n2) In Section 5.1, it would be good to describe more about the process of annotating clickbait tags for instances.\n3) In Table 1, it's not clear if the results of proposed method are statistically significant compared to baselines such as KED.\n", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We much appreciate your time and valuable comments. We provide the following explanations to address your concerns.\n\n**Regarding generalizability**\n\nWe appreciate your thoughtful consideration of the potential limitation that our proposed model relies on manually designed features, which we have also discussed in **Section Limitation**. We recognize that this limitation may pose challenges and difficulties in terms of the applicability and adaptability of our model when deployed across social media platforms.\n\nFurthermore, we would like to emphasize that our model has been meticulously crafted to address the need for adaptability to manual features across different platforms.\n\n+ Flexible Model Architecture: Our model architecture is flexibly designed to accommodate different manually defined features and preferences as well as incorporate domain-specific lexicons for improved performance on different platforms, ensuring our model has the potential to be effectively applied across social media platforms.\n\n+ Adaptive Learning: Our model incorporates several advanced learning modules, such as dynamic Graph Convolutional Networks (GCN) and Multi-Head Attention. These modules empower our model to identify and adapt to various linguistic nuances and emerging trends prevalent in diverse social media platforms.\n\nIn conclusion, we sincerely appreciate the thoroughness with which you have reviewed and considered our proposed model. We will fully consider the potential limitation regarding the model's applicability across social media platforms and assure you that we are committed to addressing this limitation in our future work. We recognize the significance of studying cross-platform or cross-language click-bait detection models as a captivating avenue for future research. \n\n**Q1: In practice it would be more interesting to explore multimodal data sources such as images for the problem.**\n\nWe highly value your valuable suggestion, which sheds light on an interesting direction. It is true that incorporating multimodal data sources, including images, has the potential to enhance clickbait detection, a point we have also addressed in **Section Limitations** of our discussion. In fact, considering the multimodal nature of clickbait is a topic that we plan to focus on and improve in our future endeavors.\n\nIn addition, the existing benchmark datasets for clickbait detection currently suffer from a shortage of image data. For instance, the FNC Challenge dataset does not include any images, and although the Clickbait Challenge17 datasets do contain some images, they represent only a small fraction, approximately one quarter, of the total samples. The lack of sufficient image data poses significant challenges for the clickbait detection community in effectively incorporating multimodal data sources. Most recent methods for clickbait detection primarily emphasize the analysis of textual information. Accordingly, we did not consider multimodal clickbait detection models in this work, and we are dedicated to expanding our future research in this direction to advance the field of clickbait detection.\n\n\n**Q2: In Section 5.1, it would be good to describe more about the process of annotating clickbait tags for instances.**\n\nThanks for your valuable suggestion. Considering the page limitations, we have made a deliberate decision to prioritize providing detailed explanations of our model to provide a better understanding and ensure complete reproducibility of our model for future researchers. Note that the two datasets in our experiments are widely used and publicly available for clickbait detection, both of which come with pre-annotated labels.\n\nHerein, we provide a thorough study and analysis of these datasets:\n+ FNC Challenge dataset: The dataset comprises three parts: headline, body, and labels. Building upon the existing labels {unrelated, discuss, agree, disagree}, we followed [1,2] to further classify them into {clickbait, non-clickbait}. Specifically, we defined \"unrelated\" as clickbait and the remaining three stances as non-clickbait in accordance with the dataset's description.\n+ Clickbait Challenge 17 dataset: As described in its paper [3] and related blogs, to mitigate publisher and topical biases, a maximum of ten posts per day and per publisher were sampled. All posts were annotated on a 4-point scale [not click baiting (0.0), slightly click baiting (0.33), considerably click baiting (0.66), heavily click baiting (1.0)] by five annotators from Amazon Mechanical Turk.\n\nWe will supplement these details in the final version. We believe that by including this information in Section 5.1, we enhance the transparency of our dataset annotation process and facilitate a better understanding of the two datasets.\n\n[1] An Attention-Based Neural Network Using Human Semantic Knowledge and Its Application to Clickbait Detection. IEEE Open J. Comput. Soc. 3: 217-232 (2022).\n\n[2] Clickbait detection with style-aware title modeling and co-attention. ACL 2020.\n\n[3] Crowdsourcing a Large Corpus of Clickbait on Twitter. COLING 2018.\n\n**Q3: In Table 1, it's not clear if the results of the proposed method are statistically significant compared to baselines such as KED.**\n\nIn Table 1, we compared the baselines with our proposed MCDM and its two variants (SFP and OPCR):\n+ The Subjective Feelings Preference-based Clickbait Detection Model (SFP) leverages subjective sentiment cues such as emotional tendencies and writing style to identify posts with inducing (clickbait) characteristics.\n+ The Objective Content Relationship-based Clickbait Detection Model (OPCR) utilizes entity information related to the post's news elements and combines it with corresponding article content.\n+ The Model Combining Subjective and Objective Perspectives (MCDM) integrates both subjective and objective viewpoints.\n\nFrom Table 1, we can observe that MCDM outperforms almost all baselines (including the state-of-the-art baseline KED) on both two datasets in terms of all four metrics (accuracy, precision, recall, and F1 score). For example, we show the corresponding results of our MCDM and KED in the following table (copied from Table 1). Herein, we highlight the **statistically significant improvement** (i.e., **two-sided t-test with p < 0.05**) indicated by $*$. These impressive results show significant advantages of our proposed MCDM over KED. Also, MCDM achieved more substantial improvement over other clickbait detection models. Our MCDM's advantages stem from its incorporation of three main features: comprehensive subjective and objective content representations, subjective sentiment features, and news entity preference feature that enhance the model's ability to handle scenarios with unclear subjective sentiments. \n\n| | |Clickbait |Challenge17  | | | FNC | Challenge | |\n|:---|:---|:---|:---|:---|:---|:---|:---|:---|\n| Methods| Accuracy| Precision| Recall| F1|Accuracy| Precision| Recall| F1|\n|KED | 0.873 | 0.761 | 0.745 | 0.753 | 0.918 | 0.927 | 0.913 | 0.925|\n|MCDM(ours) | **0.881*** | **0.782*** | **0.754*** | **0.768*** | **0.927*** | **0.958*** | **0.922*** | **0.945*** |\n\nIn summary, our model's performance improvement over the baselines is multi-faceted and comprehensive, indicating the significant superiority of MCDM over the baselines.\n\nThank you for your positive comments and valuable suggestions. We hope our explanations can address all your concerns.", "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": "1) This paper presents a multiview clickbait detection model, which can employ both subjective and objective preference features for the clickbait prediction.\n2) The work relies on a multi-head attention strategy to extract contextual information in articles, which can capture the comprehensive clickbait clues in posts.\n", "reasons_to_reject": "In practice, it may lead to limited generalizability to rely on manually defined list of features and preferences, or selected lexicons, especially applying the proposed method across social media platforms.", "questions_to_authors": "1) This work aims to address clickbait detection task, and formulates it as a text classification problem. In practice it would be more interesting to explore multimodal data sources such as images for the problem.\n2) In Section 5.1, it would be good to describe more about the process of annotating clickbait tags for instances.\n3) In Table 1, it's not clear if the results of proposed method are statistically significant compared to baselines such as KED.\n", "paper_topic_and_main_contributions": "This paper is about clickbait detection on social media, which is an important task in practice. It presents a multi-view clickbait detection model, which can capture both subjective and objective preference features to address the problem. The proposed method can employ the emotional characteristics of posts from the subjective viewpoint and the clickbait features from the objective perspective via anlayzing both post and article content. The work conducts extensive experiments on the two real-life datasets, and shows that the proposed method outperforms the selected baselines. This paper was well written and is easy to follow.\n", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "NA", "Typos_Grammar_Style_and_Presentation_Improvements": "NA", "Justification_for_Ethical_Concerns": "NA"}, "extra_text": {}}, {"review_id": "OMBZBbPPpM", "reviewer": "Reviewer_7Cfx", "summary": "", "questions": "Can you include more recent baseline methods?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We much appreciate your time and valuable comments. We provide the following explanations to address your concerns.\n\n**1. It would be better if more real-world datasets are used in experiments.**\n\nThank you for your suggestion. Due to time limitations, it is hard for us to report the results of all comparative methods on new real-world datasets. But, we will perform experiments on new real-world datasets, e.g., News Clickbait Detection (News) [4] and News Headline Incongruence Detection (NELA) [4], and report the corresponding results in the final version.\n\nIn this work, our selection of datasets in this work is inspired by [1, 2, 3]. Both the datasets we utilized are sourced from real-world Twitter and corresponding news websites, collected by data providers without any filtering or processing.\n\n[1] An Attention-Based Neural Network Using Human Semantic Knowledge and Its Application to Clickbait Detection\n\n[2] Clickbait detection with style-aware title modeling and co-attention, CCL, 2020.\n\n[3] Similarity-aware deep attentive model for clickbait detection, PAKDD, 2019.\n\n[4] Clickbait Detection via Contrastive Variational Modelling of Text and Label, IJCAI 2022.\n\n**2. There are a few flaws in paper writing. First, the font in many figures is too small, making it difficult to read. Second, there should be some punctuation at the end of the equations.**\n\nThank you for your valuable suggestions.\n\n+ Firstly, we apologize for the inconvenience caused by the small font size in certain figures, which may have affected the readability. We will redraw the figures and ensure that the text and labels are clearly legible, enhancing the overall quality of the visuals.\n\n+ Secondly, we understand the importance of proper punctuation at the end of equations. In the revised version of the paper, we will ensure that all equations are appropriately punctuated for clarity and accuracy.\n\nWe assure you that we will thoroughly review and polish the paper to guarantee its overall quality. We are dedicated to making these improvements and delivering an enhanced final version.\n\n**3. Can you include more recent baseline methods?**\n\nYes, we will include three recent baseline methods, i.e., LSACD [7], RCD [5], and CVM [6], in the experiments. We report their performance on the two datasets in the following table.\n\n| | |Clickbait |Challenge17  | | | FNC | Challenge | |\n|:---|:---|:---|:---|:---|:---|:---|:---|:---|\n| Methods| Accuracy| Precision| Recall| F1|Accuracy| Precision| Recall| F1|\n|LSACD [7]| 0.846 | 0.742 | 0.698 | 0.797 | 0.913 | 0.836 | 0.847 | 0.825|\n|RCD [5]| 0.857 | 0.769 | 0.756 | 0.791 | 0.862 | 0.772 | 0.824 | 0.743|\n|CVM [6]| 0.860 | - | - | 0.694 | - | - | - | -|\n|MCDM(ours) | **0.881*** | **0.782*** | **0.754*** | **0.768*** | **0.927*** | **0.958*** | **0.922*** | **0.945*** |\n\nFrom the table, we can observe that our proposed MCDM outperforms the recent baselines, indicating the effectiveness of our proposed model.\n\nThank you for your suggestion. We will report the corresponding results in the final version to deliver a more convincing experiment evaluation.\n\n[5] Rumor and clickbait detection by combining information divergence measures and deep learning techniques, ARES 2022.\n\n[6] Clickbait Detection via Contrastive Variational Modelling of Text and Label, IJCAI 2022.\n\n[7] A deep model based on Lure and Similarity for Adaptive Clickbait Detection, KBS 2021.\n\n\nThank you for your positive comments and valuable suggestions. We hope our explanations can address all your concerns.", "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": "1. The idea of modelling subjective feeling for clickbait detection is novel and clever.\n\n2. The proposed method to utilize both subjective feeling and objective content relevance is solid and reasonable.\n\n3. The experiments are comprehensive and convincing.", "reasons_to_reject": "1. It would be better if more real-world datasets are used in experiments.\n\n2. There are a few flaws in paper writing. First, the font in many figures is too small, making it difficult to read. Second, there should be some punctuation at the end of the equations.", "questions_to_authors": "Can you include more recent baseline methods?", "paper_topic_and_main_contributions": "The authors propose a clickbait detection model called MCDM to address the significant gap in clickbait detection. MCDM consists of two novel modules: a subjective feeling module and an objective content relevance module. The subjective feeling module captures user-centric features of posts, including language patterns and emotional inclinations. On the other hand, the objective module examines news elements and models article content correlations to identify objective clues for clickbait detection. The experimental results on two real-world datasets demonstrate that MCDM outperforms state-of-the-art approaches for clickbait detection. This confirms the effectiveness of integrating subjective and objective preferences in detecting clickbait content.", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "None", "Typos_Grammar_Style_and_Presentation_Improvements": "First, the font in many figures is too small, making it difficult to read. Second, there should be some punctuation at the end of the equations."}, "extra_text": {}}]

Author rebuttal:

Meta-review: The reviewers saw both strengths and weaknesses in the submitted version of the paper. The authors have provided a rebuttal that appeared to alleviate some concerns. After some discussion, the overall verdict was leaning toward an acceptance of this submission. However, there are still some issues to be addressed as raised by Reviewer mx4P. So, a suggestion was made to accept the paper as Findings. The reviewers' concerns should be considered in the final version.
Decision: Accept-Findings