BanglaAbuseMeme: A Dataset for Bengali Abusive Meme Classification
Authors: Mithun Das
Animesh Mukherjee
Venue: EMNLP 2023 Main (2023)
Keywords: abusive meme
low-resource language
social media

Abstract: The dramatic increase in the use of social media platforms for information sharing has also fueled a steep growth in online abuse. A simple yet effective way of abusing individuals or communities is by creating memes, which often integrate an image with a short piece of text layered on top of it. Such harmful elements are in rampant use and are a threat to online safety. Hence it is necessary to develop efficient models to detect and flag abusive memes. The problem becomes more challenging in a low-resource setting (e.g., Bengali memes, i.e., images with Bengali text embedded on it) because of the absence of benchmark datasets on which AI models could be trained. In this paper we bridge this gap by building a Bengali meme dataset. To setup an effective benchmark we implement several baseline models for classifying abusive memes using this dataset. We observe that multimodal models that use both textual and visual information outperform unimodal models. Our best-performing model achieves a macro F1 score of 70.51. Finally, we perform a qualitative error analysis of the misclassified memes of the best-performing text-based, image-based and multimodal models.

BanglaAbuseMenee A Dataset for Bengali Abusive Meme Classification
Mithun Das
Animesh Mukherjee
IIT Kharagpur West Bengal, India mi mithndaas@iiee tkg.. ac. in
IIT Kharagpur West Bengal, India animeshm@cse wwtpicceee.iikeee wwtkkp..aa.......
Abstract
target communities. Such memes collectively known as abusive memes on social media. Owing to their naturally viral nature, such abusive memes can ignite social tensions, tarnish the reputation of the platforms that host them (Statt, 2017), and may severely affect the victims psychologically (Vedeler et al., 2019). Therefore, controlling the spread of such abusive memes necessary and the first step toward this is efficiently detect them.
The dramatic increase the use of social me- dia platforms for information sharing has also fueled steep growth in online abuse. A sim- ple yet effective way of abusing individuals or communities is by creating memes, which of- ten integrate an image with a short piece of text layered on top of it. Such harmful elements are in rampant use and are threat to online safety. Hence necessary to develop effi- cient models detect and flag abusive memes. The problem becomes more challenging in low-resource setting (e.g., Bengali memes, i... images with Bengali text embedded on it) be- cause of the absence of benchmark datasets on which Al models could be trained. In this paper we bridge this gap by building Bengali meme dataset, To setup an effective benchmark we im- plement several baseline models for classifying abusive memes using this dataset'. We observe that multimodal models that use both textual and visual information outperform unimodal models. Our best- pest-performing model achieves a macro F1 score of 70.51. Finally, we per- form qualitative етrоr analysis of the misclas- sified memes of the best- best- pereooreff text-based, image-based and multimodal models.
In the past few years a number of studies have tried to take initiative to detect and control the effect of abusive memes on different social media plat- forms. However most of these are concentrated around memes that have English as the text com- ponent (Sabat et al., 2019; Gomez et al., 2020; Suryawanshi al., 2020; Pramanick et al., 2021a). Further, several multimodal vision language mod- els have been explored, but again they are limited English. Efforts in other languages is low and this is particularly true for resource- iosuurcc-impverisheed lan- guages like Bengali (aka Bangla). In this paper we consider the problem of detecting Bangla abusive memes (see Figure 1 for examples) across social media platforms. The motivation comes from the fact that Bangla is the seventh most spoken lan- guage (ber) in the world having speakers, with around 100 million Bengali speak- ers in Bangladesh and about 85 million speakers in India. It is the official language of Bangladesh and one of the officially recognized languages in the constitution of India. Besides Bangladesh and India, Bengali is spoken in many other countries, in- cluding the United Kingdom, the United States, and several countries in the Middle East (Britannica, 2022). The other important reason for considering Bangla is linked the several smearing incidents in Bangladesh and India, such as slandering moves against famous political leaders, celebrities, and so- cial media personalities, online anti-religious pro- paganda, and cyber harassment (Das et al., 2022) that have inflicted the online world.
Disclaimer: This paper contains elements that one might find offensive which cannot be avoided due to the nature of the work.
210 million
1 Introduction
In recent times, Internet memes have become com- monplace across social media platforms. A meme is idea usually composed of image and short piece of text on top of it, entrenched part of the image (Pramanick al., 2021b). Memes are typi- cally jokes, but in the current Internet culture they can be far beyond jokes. People make memes as they are free of cost but can impress others and help to accrue social capital. Bad actors however use memes to threaten and abuse individuals specific
We make our code and dataset public for others on https: /gg thub. cww/hate-aaeeeeeeo alert. Manglalbuseeeen

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CIामM शर म निi भाभी
2
Related work
Abusive meme datasets: In an effort to develop re- sources for multimodal abusive meme detection, several datasets have been constructed. Sabat et al. (2019) created dataset of 5,020 memes for hate meme detection by crawling Google Im- ages. Gomez et al. (2020) contributed another multimodal dataset (MMHS 50K) for hate speech detection which has 150K posts collected from Twitter. Similarly, Chandra et al. (2021) developed a dataset for detecting antisemitism multimodal memes by crawling datasets from both Twitter and Gab. Suryawanshi et al. (2020) created another dataset of 743 memes annotated as offensive not-offensive. The dataset has been built by lever- aging the memes related to the 2016 US presiden- tial election. Pramanick al. (2021a) also built another dataset to detect harmful memes contain- ing around 3.5K memes related to COVID-19. addition, to boost the research around multimodal abusive memes, several shared tasks have been or- ganized. Facebook AI (Kiela et al., 2020) intro- duced another dataset of 10K+ posts comprising la- beled hateful and non-hateful as part of the Hateful Memes Challenge. There are two previous works on Bengali meme detection. The first work, con- ducted by Karim al. (2022), involved extending the Bengali hate speech dataset (Karim et al., 2020) by labeling 4,500 memes. Hossain et al. (2022) developed a dataset of 4,158 memes with Bengali and code-mixed captions. In the former work the authors have not made the data public (although they put github link in the paper albeit without the link to the dataset). Further the data collection and annotation process is not discussed detail, the target communities are not labeled and the inter- annotator agreement is also not reported. The latter work does not make the data public. Here again the target labels are missing making it difficult to ascertain how diverse the data is.
For costin pet all your silk (Targe Geodes Sancaom Valgar You, Seatiment Neyaliv))
Kanpladinst enalisle degccatingg
(Targe:: Natimaal origns Religion, Sureasm:
Vilgar You Sentietcnt Nozzine
Figure 1: Examples of abusive memes.
Our key contributions in this paper are as fol- lows.
To bridge the gap in Bangla abusive meme research we release a dataset- - BanglaA buse- Meme - to facilitate the automatic detection of such memes. The dataset comprises gold annotations for 4,043 Bengali memes, among which 1,515 memes abusive while the rest non-abusive. Further, each instance has been marked with the following labels (a) whether vulgar or not, (b) whether sarcastic or not, (c) the sentiment in the meme (posi- tive, negative and neutral, and (d) the target community being attacked. We believe that our comprehensive annotations will enable us to gain deeper understanding of the dynam- ies of Bengali memes well as help future research.
We implement several baseline models iden- tify abusive memes automatically. The model variants include purely text-based, purely image-based, and fusion-based multimodal approaches. We observe that our multimodal model CLIP(L) outperforms the other vari- ants achieving overall macro F1-score of 70.51. We also observe that the multimodal model performs well for most of the target communities, while this not true for the text/image-based basclines.
In our work, we aimed to overcome the draw- backs of these previous studies by not only labeling memes as abusive or non-abusive but also further annotating them with labels such as vulgar, sar- casm, sentiment, and the targeted community. This richness of our data provides more holistic under- standing of the dynamics of Bengali memes.
We perform qualitative error analysis of a sam- ple of memes of the best text, image and mul- timodal models where the models misclassify some of the test instances. While bated approaches fail in the absence of toxic words, image-basee approaches fail when the image is typically out-of-context. The multimodal model fails when the abuse implicit na-
Multimodal abusive meme defection: With re- gards to abusive meme detection, several tech- niques based on diverse model architectures have been investigated. Sabat et al. (2019) used a
ture.

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Abueve Nee-atusive
120
Frequency
60
100
20
Gerdder
weigloo
mutional prigin
Indwitiual
Sestal Sull-grerr
02terr
20
50
Frequency count of wards
Figure 3: Histogram of the word count of the meme text for each class.
Target
Figure 2: Distribution data points across the target communities.
challenge (Kiela et al., 2020), which contributed synthetically generated memes, our dataset consists of memes curated from the real-world. Once the images are downloaded, we apply the following data sampling techniques before moving forward with the actual annotation. We remove the memes having no text or having text in different languages other than Bengali. We also remove memes having very low resolution where the meme text unread- able.
plethora of models, including BERT, VGG16, and MLP. Gomez et al. (2020) explored several mod- els such as FCM, Inception-V3, LSTM, etc. With the advancement of the vision language models, several approaches, such as VisualBERT (Li et al.. 2020), ViLBERT (Lu et al., 2019), MMBT (Kiela et al., 2019), UNITER (Chen et al., 2020), and others (Velioglu and Rose, 2020; Pramanick et al., b021b; Chandra et al., 2021) have been explored. Recent studies have also attempted several data augmentation techniques (Velioglu and Rose, 2020; Lee al., 2021) improve classification perfor- mance. While previous methods were proposed for abusive speech detection for the English language, we focus on detecting Bengali abusive memes.
Annotation strategy: To identify whether meme abusive non-abusive, we hire five undergradu- ate students for our annotation task: three of them were males, and the other two were females, and they are all in the age range 24 to 29 years. All the undergraduate students are native Bengali speakers and have been recruited on voluntary basis. We paid them fairly for their work as per the standard local compensation rate?. The anno- tation process was supervised by an expert Ph.D. student with over four years of experience dealing with malicious social media content. Each meme in our dataset contains five kinds of annotations. First, whether the meme is abusive or not. Second, the target communities of the meme, if any. The targets in our dataset belong to one of the follow- ing seven categories gender, religion, national origin, individual, political, social sub- sub-groups, and others. Third, whether the meme is vulgar or not. Fourth, whether the meme is sarcastic or not. Last, the sentiment labels (positive/neutraal/egati as- sociated with the meme. For non-abusive memes, if the target is absent, no target label is assigned to that meme.
3 Dataset creation
Here we discuss the data collection strategy, anno- tation guidelines, and the statistics of the annotated dataset.
Data collection and sampling: In order con- struct the abusive meme dataset, we first build a lexicon of offensive Bengali terms. This lexicon comprises words that target individuals or different protected communities. In addition, we include the targeted community's 's name in the lexicon. The choice is made to extract random hateful/offensive memes about community that do not contain abu- sive words. Then, using these lexicons, similar to Pramanick et al. (2021a) we perform keyword- based web search on various platforms such as Google Image, Bing, etc. We also scrape various pages and groups from Facebook and Instagram. To download the images, we used an extension2 of Google Chrome. Unlike the Hateful Memes
Annotation codebook: In order to ensure accu- rate and consistent annotation, detailed annotation guidelines are essential for annotators to determine suitable labels for each meme. achieve this, we
"https://donnlodd" all- images.aee..... amages.mobilelteeee.m....... mobilefirst.me/
We gave them one Indian rupee for annotating each meme.

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Abusive
#
Non-sbushre
this task, which is deployed on Heroku instance. We provided a secure account each annotator where they could annotate and track their progress.
HT (No)
201
41 (No)
379
ecx (Ry deing)
188
<9C (Tly dking)
310
oga (And)
180
VNTS (And)
251
ensen (Rangal)
10k
wuffi (1)
I82
ogf (1)
38
(So)
152
(Cihon))
MNTRORT (My)
140
cal (8e)
.IR (Thiss
137
Based on the guidelines provided, three indepen- dent annotators have annotated each meme, and then majority voting was applied to determine the final label. Initially, we provided a small batches of 100 memes for annotation and later expanded it to 500 memes as the annotators became more effi- cient. After completing each batch of annotations, we discussed the errors they made in the previous batch to preserve the annotators' agreement. Since abusive memes can be highly polarizing and ad- verse, the annotators were given plenty of time to finish the annotations. To choose the target com- munity of a meme, we rely on the majority voting of the annotators. Exposure to online abuse could usher in unhealthy mental health issues (Ybarre et al., 2006; Guardian, 2017). Hence, the annota- tors were advised take frequent breaks and not do the annotations in one sitting. Besides, we also had weekly meetings with them to ensure that the annotations did not affect their mental health.
R (Whot)
34
(Wibrr)
127
231 (1-)
2,33 (Ricssme)
120
ferg (finddu
82
CMES (From)
116
Table I: Top- 10 most frequent words per class.
have developed a comprehensive annotation code- book that provides clear instructions and examples, enabling all participants to understand the labeling task effectively. Our codebook incorporates annota- tion guidelines and numerous illustrative examples tailored explicitly for identifying abusive memes, We shared the annotation codebook with the an- notators and performed multiple training rounds. Through these training sessions, we familiarized the annotators with the guidelines and equipped them with the necessary knowledge to determine appropriate labels for the given memes (see Ap- pendix A for more details).
Annotation procedure: Prior to the start of the actual annotation, we require a pilot gold-label dataset to guide the annotators, as mentioned above. Initially, we annotated 100 memes, out of which 60 were abusive and 40 were labeled as non-abusive. We take ten samples from each label and incorpo- rate them into the annotation codebook, and the rest data points are used for evaluating the trial an- notation discussed below.
Final dataset: Our final dataset consists of 4,043 Bengali memes, out of which 1,515 have been la- beled as abusive and the remaining 2,528 as non- abusive. Next, 1,664 memes are labeled as sar- castic, while the remaining 2,379 are labeled not sarcastic. Further, 1,171 memes are labeled as vulgar, while 2,872 memes are labeled as not vulgar. Finally, 592 memes are labeled as having a positive sentiment, 1,414 memes as neutral, and 2,037 memes as having a negative sentiment. We achieved an inter-annotator agreement of 0.799, 0.801, 0.67, and 0.72 for the abusive, vulgar, sar- casm, and sentiment labeling tasks, respectively, us- ing the Fleiss' score. These scores are better than the agreement scores on other related hatc/abusive speech tasks (Ousidhoum et al., 2019; Guest et al., 2021). We show the target distributions of the dataset in Figure 2. In Figure 4, we show the over- lap between the -non)-abusive vs. (non)-sarcastic. We observe that a meme an abusive meme might not be necessarily sarcastic. Figure 5 shows that most abusive posts are also vulgar. In Figure 6, we show the overlap between the abusive vs. sen- timent and non-abusive vs. sentiment classes. It indicates that abusive memes mostly have negative sentiments, whereas abn-abusive memes are mostly positive or neutral.
Trial annotation: During the trial annotation task, we gave the annotators 80 memes and asked them to label the memes according to the annotation guidelines. We instructed the annotators to keep the annotation codebook open while doing the an- notation to have better clarity about the labeling scheme. After the annotators finished this set, we consulted with them regarding their incorrect anno- tations. The trial annotation an important stage for any dataset creation process as these activities help the annotators better understand the task by correcting their mistakes. In addition, we collected feedback from annotators to enrich the main anno- tation task.
Main annotation: After the trial stage, we pro- ceeded with the main annotation task. We use the open-source data labeling tool Label Studio for
"https://labelstud"

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Normltes Samples
Nomakoon Sanple
Abusive Nauuivvvivee
Abusvo
2.
05--
0.5
No
0.0
Sarcasm
Labele
Posttive
Neutral Sentiment
Negative
Figure 4: Abusive X Sarcasm.
Figure 5: Abusive X Vulgar.
Figure 6: Abusive X Sentiment.
M
Model
Acc
M-F1
FI(A)
P(A)
R(A)
Abusive Noniabusive
m. BERT!
64,50
61.53
51.13
53.05
50.15
MURIL
66.41
65.18
58.84
54.44
64.43
nomal 2ed ampath
0.003
BanglaBERT
XLMR!
67.79
65.73
57.03
57.09
59.03
VGGI6
67.47
64.77
55.21
57.54
53.80
0.003
ResNer-152f
VIT!
69.37
67.72
60.50
58.73
62.71
0.002
VAN
66.33
64.64
57.31
55,24
60.21
0.001
MU-BNIC)
XLM-RN(C)
69.30
67.13
58.75
59.19
58.48
0.000
MU-VITCC
69.62
67.15
$8.29
601.40
$6.95
hate crimas cereeoi Boeeefi frll wont
out
fun AoAtLU Porre
T
XIM-VITIC)
69.57
68.19
61.62
58.49
65.28
CLIPIC)
72.02
69.92
62.01
63.20
60.98
MU-RNIL)"
67.22
64.72
55.36
56.73
54.19
X
69.27
66.77
$7.74
59.98
56.05
MU-VITI)'
68.19
66. 13
57.82
57.47
58.28
texts using Em- path. We report the mean values for several categories Empath. * indicates (p < 0.01) using the M-W test (McKnight and Najab, 2010).
Figure 7: Lexical analysis the
XLM-VITO)
68.95
66.26
56,80
59.44
54.73
CLIPLY"
71.78
70.51
64.60
61.44
68.70
Table 2: Performance comparisons across the different models. A: abusive class, P: precision, R: recall. The best performance each column is marked bold, and the second best is underlined. The best performing CLIP((I) model denoted by * is significantly different compared other models marked by f as per the M-W U test (p < 0.05).
3.1 Text analysis
We perform the following analysis obtained from the meme text.
Distribution of word length: Figure shows the average length of the extracted meme texts in terms of the number of words per class. We plot the his- togram of word counts and find that both abusive and non-abusive meme texts follow a similar dis- tribution. Further, we notice that most meme texts have lengths between 10 and 15 words.
texts using Empath (Fast et al., 2016), which has 189 such pre-built categories. First, we select cat- egories excluding the topics irrelevant abusive speech, e-g., technology and entertainment. We report the relevant categories in Figure Abusive meme texts scored high in categories like 'hate', 'crime', 'suffering', 'fight', 'war' and 'wcapon'. Non-abusive meme texts score higher on topics such as 'negative emotion (neg_emo)', 'giving', "achievement" and 'fun'.
Most frequent words: Table shows the top 10 most frequent words in the meme texts of the an- notated dataset for each class. Before counting the most frequent words, 2021) library to remove the stop words. We observe that the target communities' names, such as Ghoti, Bangal, Hindu, etc., are more prevalent in the abu- sive class. We also create word clouds and find the presence of abusive words like 'Malaun' (hateful word targeting 'Bengali Hindus'), 'Kangladeshi' (hateful word targeting 'Bangladeshi',, 'Rendi' (bitch), etc., in the abusive class. The presence of such words is relatively less in the non-abusive class.
the BNLP (Sarker,
3.2 Image analysis
One of the important component in any meme is the presence/absence of facial image. Hence, we attempt analyze faces that are present in the meme. For this purpose, we use the FairFace li- brary (Karkkainen and Joo, 2021). For given image, first, we check if a face present or not. If present, we further study the gender and age associated with the face. We observe that 33.2% of memes have no faces recognized, out of which
Empath analysis: In order to understand the dataset better, we further identify important lex- ical categories present in the OCR extracted meme
Transsated to English using Google translator.

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Sorve ISRUCC
FONETT
Clean Scaatin
DDX
JUM Boteee
MARI
Lape
(b) Late fusion
(a) Concatenation
Figure 8: Fusion based models.
Target
FI(A)
P(A)
R(A)
cussed below.
Gender
40.11
40.09
41.39
74.30
28.68
Religion
61.62
54.31
72.58
Text-based classification: Here, the idea is to classify the memes solely based on the embed- ded text presented as part of the image. We use Easy-OCR" to extract the texts of meme and apply standard pre-processing techniques on the post to remove URLs, mentions, and special characters. We experiment with the following transformer-basee models for the classification, m- BERT (Devlin et al., 2019), MurII. (Khanuja et al., 2021), XLM-Roberta (Conneau et al., 2020) and BanglaBERT (Bhattacharjee et al., 2022).
National
Origin
50.90
47.50
60.86
86.59
46.92
Individual
57.91
52.81
37.29
69.86
25.43
Political
50.00
48.42
39.40
28 80
62.35
Social
Sub-groops
54.69
51.97
40.55
63.72 74
29.73
Others
56.89
50.00
Table 3: Zero-shot target-wise performance.
XIMR
CLIPEU
0.7
Image-based classification: Here we pose the problem as an image classification task and ex- periment with the following pre-trained mod- els: VGG16 (Simonyan and Zisserman, 2014), ResNet-152 (He et al., 2016), Vision trans- former(VIT) (Dosovitskiy et al., 2021) and Visual Attention Network (Guo et al., 2022).
0.3
0.2
Religton
bational Orign
Indivanas
Pastoral
Others
Rooa Bab
Target
Figure 9: Target-wise performance. Only the best- performing models for each modality are shown.
Multi-modal classification: The unimodal models we discussed far cannot leverage the relationship between the text and image in the meme. Thus, we attempt to combine both modalities meaningfully to capture the benefits of textual and visual fea- tures. We experiment with two different techniques of fusion - (1) Concatenation (C) and (2) Late fu- sion (L). For concatenation, the pre-trained features from both text-based and image- based models are concatenated and then passed through the MLP for the classification (see Figure (a)). In the case of late fusion, the extracted text and image features are fed through MLP first, and then the intermedi- ate layers' features are concatenated, and a final MLP is used for classification (see Figure 8 (b)). We choose the top two best-performing text and image-based models and construct the following fusion variants- (i) MurlL + VIT, (ii) Murll, ResNet-152, (iii) XLMR + VIT, (iv) XLMR ResNet-152. We also used CLIP (Radford et al., 2021), a pre-trained linguisic model to cap-
13.3% are from the abusive class. Further, we ob- serve that the mean number of faces for the abusive and non-abusive classes are 2.1 and 2.43, respec- tively. In the abusive class, 58.45% of faces are male, 41.54% are female, and around 68. 68.24% of the female faces are between the 20-39 age group. For the non-abusive class, 73.20% of faces are male, and 26%79% are female; the female age group 20-39 makes up around 73.87% of the faces in this class. The key observation is that the % of females in the abusive class is double that in the non-abusive class, which possibly indicates that girls/women are extremely frequent victims of abuse.
4 Methodology
This section presents a suite of models we have used for Bengali abusive meme detection ranging from text-based, image-based, and, finally, fusion- based multimodal models. The techniques are dis-

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Musbe
Vonabutiee
31
87
Sarcur ALLihiIA
Wager
Figure 10: Sarcasm label-wise performance.
Figure 11: Vulgar label-wise performance.
Figure 12: Sentiment label-wise performance.
ture the overall semantics of the memes due to its excellent performance in several harmful meme classification tasks (Maity et al., 2022; Kumar and Nanadakumar, 2022).
FI(A)
R(A)
Hindi
Hindi
71.04
71.46
75,77
75.20
72.40
Bengali
51.87
Benguli
Bengali
70.08
69.28
64.30
58.44
71.47
Bengali
Hindi
50.46
45.07
27.86
70.44
17.36
Table Comparison with existing dataset another language (Maity et al., 2022).
4.1 Experimental setup
Experimental results
5.1 Abusive meme detection
We evaluate our models using k-fold stratified cross-validation. For all the experiments, k here, and for each fold, we use 70% data for training, 10% for validation, and the rest 20% for testing. We use the same test set across all the models to ensure fair comparison. For all the uni- modal neural network models, the internal layer has two fully connected dense layers of 256 nodes, reduced to feature vector of length 2 (abusive non-abusive). We have two variants for fusion- first con-
Table 2 shows the performance for all the mod- els. From the table, we observe the following. Among the text-based models, XLMR performs the best with an accuracy of 67.79% and a macro-Fi score of 65.73%. The MurlL model performs the second best in terms of macro-F1(65.. 18%) score. For image-based models, the VIT model performs the best (ace: 69.37%, macro-Fl: 67.72%) and the ReseNet-152 model performs second best (ace: 67. 77.8 macro-Fl: 64.94%). In the multimodal setup, we notice CL.P((L. exhibits the best perfor- mance in terms of macro-Fl score (70.51%) closely followed by CLIP(C) (macro-F1: 69.92%) at the second best position'.
set
based techniques. For concatenation, catenate the text and image embeddings and pass the concatenated features through two fully con- nected dense layers 256 nodes, which further maps to a feature vector of length 2. For the late fusion, the unimodal text and visual features are separately passed through two dense layers of size 256, which are finally fed to another dense layer of 64 nodes. Finally, concatenate all the nodes and reduce them to feature vector of length 2. In addition, we conduct an out-of-targed experiment to evaluate how our model performs for an unseen target community. We exclude one target commu- nity from our training and validation data and build our model using the remaining target communities. We call this the zero-shot setting for the excluded target. We then assess the model's performance that excluded target community. This experiment is conducted using our best classification model. For this experiment, we use 85% of the data for training and 15% for validation (see Appendix for more details).
5.2 Target-wise performance
Figure 9 shows the target-wise performance of the best unimodal and multimodal models. We ob- serve that the performance of the unimodal mod- els varies across all target communities, while the multimodal model consistently reports good per- formance across all target communities except the political target community.
Zero-shot: In Table 3, we present the performance of zero-shot target-wise classification for the best multimodal CLIP(L) model. Except for the "Oth- ers" category, all other targets exhibit macro F1-
"We also investigate the performance of multi-task model where abusive meme detection is the main task and vulgarity detection, sentiment classification and sareasm detection are used as auxiliary tasks. Please refer to Appendix E for the results obtained from this model.

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Figure 13: Inference results on the examples from the test set for the best text-based, vision-based, and multimodal models.
scores around or below 50%. While the precision and F1-score for the abusive class is high for the religion target, the increased misclassification in the non-abusive class adversely affects the overall macro Fl-score. This finding underscores the need for more generalizability in abusive meme detec- tion schemes across unknown target communitics. In other words, developing effective strategies that can enhance performance in unseen target commu- nities and improve detection capabilities is crucial.
ing of 5,854 memes created by Maity et al. (2022). Our experimentation focused exclusively on the CLIP(L) model, which exhibited superior perfor- mance among all the models considered. In this experimental setup, we trained the model using the Bengali language and evaluated its performance using the Hindi language and vice versa. We out- line the results in Table 48. We observe that the model trained in the Hindi language exhibits poor performance when applied to Bengali. Conversely, the model trained in Bengali demonstrates subpar results when tested in Hindi. This outcome empha- sizes the need curate new abusive meme datasets for other low-resource languages. By doing so, we can accurately detect multilingual memes low-resource languages and contribute to the ad- vancement of research in this domain.
5.3 Performance across different dimensions
In addition, we present the performance of the best multimodal model for the memes in the following buckets (i) sarcastic vs non-sarcastic (Figure 10) (ii) vulgar non-vulgar (Figure 11), (ii) positive negative neutral sentiments (Figure 12). We observe that the sarcastic memes in the abusive cat- egory exhibit poor performance compared other categorics. Further, the abusive memes the vul- gar subset shows the best performance while in the non-vulgar subset it is inferior. Finally, we observe that in all sentiment subsets the performance for non-abusive memes is similar. On the other hand, in the negative sentiment subset abusive memes are more accurately detected compared to the other two subsets.
5.5 Error analysis
In order to understand the workings of the mod- els further, we analyze the inferences of best text- based (XLMR), image-based (VIT), and multi- modal (CLIP(L)) classifiers on a small set of exam- ples from the test set. We manually inspect some of the misclassified memes by each classifier and try to provide reasons for these cases.
XLMR wins VIT fails: We observe that when the extracted OCR text contains slurs target- ing individuals or communities, or the abu- siveness in the text is clear while the image does not carry any meaningful information about the post being abusive, XLMR model
5.4 Additional experiment
In addition, we performed another experiment to understand the performance of model transferabil- ity of abusive meme detection with another lan- guage dataset. For this, we use the cyberbullying detection dataset in Hindi (code-mixed) consist-
*Here, for the Bengali meme language, we show the perfor- mance of fold with which transferubility has been performed.

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performs better.
model accurately classifies the meme as abusive and directed toward obese women. Figure 13(d) depicts meme where the text and image conveys offensiveness toward Pakistanis with the text con- taining the hateful term 'Pakistan murdabad'. The accompanying image displayed individuals plac- ing their feet on the Pakistani flag, deeply dis- respectful act. Consequently, all models correctly classify this as abusive. Continuing Figure 13(e), an image portrays man seemingly experiencing hangover. However, the meme insinuates that the former cricket coach, Ravi Shastri, is obsessed with an alcohol problem and meant to bully Ravi Shastri. Interpreting such memes needs intricate reasoning abilities and contextual knowledge.
VIT wins XLMR fails: When the meme image represents sexual stereotyping toward women by objectifying their bodies, and the extracted OCR text looks benign, the vision model per- forms better.
CLIP(L) wins, unimodal models fail: These are the cases when the fusion-based multi- modal model predicts the actual label cor- rectly; even the best unimodal text-based and image-based model fails. We observe con- sidering only the extracted text and image in- dividually, the meme does not look abusive. But when both the modalities are used jointly to decide the label, the meme can be easily demarcated as abusive. These are the cases be learnt
6 Discussion
where better representation using the two modalities together.
The abusive meme detection model can be very effective content moderation, especially when there is a surge in multimedia content over social media platforms. The auto-discard technique has become increasingly valuable, where hateful con- tent is automatically discarded from posting once flagged by abuse detection model. Other ap- proaches include alerting the user about flagged post or lowering the visibility of the flagged post. Finally, moderators can be automatically informed to counter the flagged post so that it dilutes or nulli- fies the harmful effect of the post. In order to have such an effective flagging, some challenges include the continuous availability of training data, compu- tational costs, and the imperative of upholding the model's 's sustained accuracy over time.
All three models win: When the extracted OCR text itself abusive and the image de- picts sexual objectification or clear hatred, all the models are successful.
All three models fail: These are the cases when the memes are implicitly abusive and the models miss to capture the context due to the absence of explicit hateful content. De- tecting these instances requires understand- ing sarcasm and complicated reasoning skills or background knowledge about certain situa- tions/events.
We illustrate these findings in Figure 13 by pre- senting examples of misclassified cases encom- passing various scenarios. In Figure 13(a), we encounter a scenario where the vision-based VIT model failed to capture the true message of the meme. The image seemingly portrays the Prime Minister of India, Narendra Modi, with waterfall behind him, giving impression of benign con- tent; however, the textual component contains derogatory term, 'Malaun,' directed toward Naren- dru Modi. Based on this textual context, the text- based model successfully classifies the meme as abusive. Proceeding to Figure 13(b), an interest- ing case emerged where the XLMR model mispre- dicted the meme. The text lacked any abusive ele- ments, yet the image overtly displayed sexual vul- garity toward women, and the VIT model labeled this abusive. Figure 13(c) introduces another intriguing scenario. Analyzing the image and text in isolation would not suggest abusiveness. How- ever, when considering both elements, the CLIP
Conclusion
This paper presents a new benchmark dataset for Bengali abusive meme detection. Our dataset comprises more than 4K memes collected using keyword-based web search on various platforms. We further evaluated the performance of different classifiers ranging from text-based, image-based. and multimodal models. Our experiments show that using both textual and visual modalities to- gether helps to enhance the performance by learn- ing better representation of the memes. This is further demonstrated by our in-depth error analy- sis.
Limitations
There are a few limitations our work. First, we have only used the OCR extracted textual fea-

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tures and visual representation by the pre-trained models classify the memes. We have not consid- ered additional features such as textual and visual entities, which might improve the model perfor- mance. Second, sometimes malicious users inten- tionally add random noise to their posts to deceive the classification model. We have not tested our model's performance such adversarial attacks. Third, observed that the model fails to detect memes with implicit characteristics. Detecting such memes can be challenging, often requiring intricate reasoning abilities and contextual knowl- edge. This aspect can be taken up for immediate future work. Fourth, the Bengali language incorpo- rates at least two very large dialectal variations: (a) standard colloquial Bengali (spoken in West Ben- gal) and (b) Bangladeshi (spoken in East Bengal (Bangladesh)). The British colonizers partitioned these regions based on their sociocconomic struc- ture and religion-based demography (Das et al., 2023). The hate lexicons and the target commu- nities vary based on these dialectal variations and are an additional challenge while handling the Ben- gali language. Based on the abusive meme's 's origin and the dialect in which it is spoken, the latent tar- get vary. Dialect-based meme annotation and subsequent performance analysis is an important avenue for future research, and we plan take this up in future.
research on detecting abusive memes in a low- resource language such Bengali. We only release the dataset for research purposes and do not grant a license for commercial use, nor for malicious purposes.
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Ethics statement
User privacy: Our database comprises memes with labeled annotations and does not include personal information about any user.
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Biases: Any biases noticed in the dataset are un- intended, and we have no desire to harm anyone any group. We believe it can be subjective to determine if meme is abusive; hence biases in our gold-labeled data label distribution are in- evitable. However, we are confident that the label given to the data is accurate most of the times ow- ing to our high inter-annotator agreement. Potential harms of abusive meme detection: We observed using both modalities we achieved better performance. While these results look promising, these models cannot be deployed directly on so- cial media platform without rigorous testing. Fur- ther study might be needed to check the presence of unintended bias toward specific target communi- ties.
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Preslav Nakov, and Tanmoy Chakraborty, 2021a. De- tecting harmful memes and their targets. In Find- ings of the Association for Computational Linguis- tics: ACL-UCNLP 2021, pages 2783-2796.
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It uses derogatory or racial slur words within the meme toward target community.
It uses disparaging terms with the intent to harm or incite harm.
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It makes use of idiomatic, metaphorical, col- location, or any other indirect means of ex- pression that are harmful or may incite harm and/or
It expresses violent communications.
Benet Oriol Sabat, Cristian Canton Ferrer, and Xavier Giro-i Nieto. 2019. Hate speech in pixels: Detection of offensive memes towards automatic moderation. arXiv preprint urXiv:1910.02334.
A.2 Vulgar meme annotation
We denote a meme as vulgar if the meme contains explicit sexual content, offensive slurs, or graphic violence. They may target sensitive topics, make fun of individuals or groups, or contain content that is generally considered offensive or against social norms. Memes with mild profanity or sug- gestive elements should be considered non-vulgar unless they cross the line into explicit or offensive territory.
Sagor Sarker. 2021. Bnip: Natural language process- ing toolkit for bengali language. arXiv preprint arXiv:2102.00405
Karen Simonyan and Andrew Zisserman. 2014, Very deep convolutional networks for large-scale image recognition. arXiv preprint arxiv: 1409.1556
N Statt. 2017. Youtube is facing a full-scale advertising boycott over hate speech. The Verge.
A.3 Sarcasm annotation
Shardul Suryawanshi, Bharathi Raja Chakravarthi, Mi- hael Arcan, and Paul Buitelaar. 2020. Multimodal meme dataset (multioff) for identifying offensive con- tent in image and text. In Proceedings of the second workshop trolling, aggression and cyberbullying. pages 32
We consider meme as sarcastic if
The meme's text conveys a different mcaning than its literal interpretation,
It uses ironic satirical elements that intend express something contrary the literal text.
Janikke Solstad Vedeler. Terje Olsen, and John Eriksen.
2019. Hate speech harms: social justice discussion of disabled norwegians' experiences. Disability & Sociery, 34(3):668-38..
A.4 Sentiment annotation
Riza Velioglu and Jewgeni Rose. 2020. Detecting hate speech in memes using multimodal deep learning approaches: Prize-winning solution hateful memes challenge. arXiv preprint 2rXiv:2012.12975.
Positive sentiment refers to positive per- ception, attitude, or opinion toward a partic- ular subject or situation. meme should be labeled as positive sentiment it indicates happiness, joy, excitement, admiration, appre- ciation, or any other positive emotion.
Michele I. Ybarra, Kimberly Mitchell, Janis Wolak, and David Finkelhor. 2006. Examining characteris- tics and associated distress related to internet harass- ment: findings from the second youth internet safety survey. Pediatrics, 118(4))e1
Negative sentiment refers to an expression tone that indicates negative perception attitude towards a particular subject or sit- uation. Negative sentiment in memes may involve sadness, anger, frustration, disappoint- ment, sarcasm, criticism, or any other negative emotions.
A Annotation guidelines
A.1 Abusive meme annotation
We consider a meme as abusive if

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काका
कारनाा
II माानका
कागनगर
भालन 1
CTAT P OR
भानड भाा
बाला C
Pala र["
(877
(6/
167 भगशी
(РPT
AMTT
ल
पानाकाा
(9)0
GTOR RTT
भा भालि CIAT Mi NilT
(भा( रा
a/G NI
(olia 414
4139l (6141
8h
aiG NIAiA [
(61h1a (461
GENT 4181 शााागरााा
85 भहरन
शााागर शााा
$p ক
पााा पा6 [िcाhT
Table List of words in the abusive lexicon.
Neutral sentiment refers to an expression or tone lacking strong positive or negative emo-
tion. It indicates state of indifference, objec-
tivity, or neutrality towards particular sub- ject or situation. Neutral sentiment in memes may involve statements of fact, observations, general information, or content that does not evoke strong emotions.
Seu RA Reo
weveorl quamea cwrre ineeyer
[uass sir teeching Yhio did' now to Aus
(15 25 Sengek spealers hove recevedd mittrre
dMt who alheady comtenite AND thesa)
crowiing Polidte Settaelengt informatioo
interaggtion amested.
[Label: abaiio, Target. Nomu, Seromm.
dest is dhi atrogglig mamorr. danghh)
A.5 Target-community annotation
Vulgar
If the meme directly attacks or targets a spe- cific community, identify and annotate the tar- geted community.
Figure Example of abusive & non-abusive memes.
C Implementation details
Focus on communities based on race, ethnic- ity, religion, gender, sexual orientation, any other identifiable social group.
For all the text-based models (m-BERT (De- vlin et al., 2019), MurIL (Khanuja et al., 2021), XLM-Roberta (Conneau al., 2020) and BanglaBERT (Bhattacharjee al., 2022)), ex- tracted pre-trained 768- 768-dimensional feature vec- tors from the meme text. These vectors were then passed through dense layers for the final classifica- tion. Among the image-based models, we resized the memes to size of 224 x 224 x 3, performed Gaussian normalization, and fed them through the pre-trained VGG16 (Simonyan and Zisserman, 2014) model to obtain 4096-dimensional feature vectors for each meme. Similarly, for the ResNet- 152 model, we resized the images and used the pre-trained ResNet-152 (He et al., 2016) to extract 0048-dimensional feature vectors for each meme.
If the meme does not target specific com- munity or if the target is unclear, label it as "None."
B Keywords used to crawl data
Table 5 presents the compilation of words in the abusive lexicon that used to crawl memes from the Internet. Since most of these words have no counterpart Englishh translation, we could not pro- vide the corresponding glosses. In addition, Table 6 provides detailed information regarding target community-ban based keywords.

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Target
Keywords
Gender
Gatll (Boy), (Reaa (Boys), C (Boys"). " (Male), T (Males), Te (Males"),
CRTC (Gir)) (AUSal (Girks, CHUSC (Gizls)). भार (Weamr),, भारनााा (Weawe)). मारनगा
(Women 1s), Ri (Tramsgender), BP9AA (Trunsgender), @P (Tranggendes) Freat Transgender).
[िान Trasgenders), सिाारा Trasggedees")
Religion
Re (Himd))) Roगाl (Hindoss). Re (Hindus"), ) अ6il- (Muslin), ΩलÍ (Muslimus), क (Moslions)
National
syae (India), अाााड (Jodia' b), Gा (Indian), вालल (Indiuns), Siac (Indigns') KाA9TCRM (Bangladesb), чासाा1ा (Bangladeshi), (тेकाC"* (Bangladeshis) (मR (Bangladeshis), 9[| (Pakistan), 9IR+ai (Pakistani), भालककारा (Pakistanis) 9ff (Pakistanid)
Origin
Individual
NTcerii (Khaleda), ATAe 451-1| (Mamata Banerjee), ofSit (Hassina), *Cat (upl (Narendra Modi), suffr "I Shah), O64 (Dev), fop (Jeet). žा (Srabanti). 8/ (Swastika), festie (Virat), للानीााा (laslima), 962) XTA (Pori Moni), [िा (Mithila)
Political
(0 Fits (Awami League), (8/0) 911ß (National Party), Reeff (BJP Bharatiya Janata Party), [a.5-19 (BNP Bungladesh Nationalist Party), कायन (Trimmool Congress). 19 (CPM
Commmnist Pacty of Iudia (Marxist))
Social Suh-
uft (Gholi), vafaa (Cibatis), vIfLL (Ghotis') ), 3|ह (Bangal), काडाना (Bangalis), 4[डÍ (Bangalis)) ) [िशा (Bilari), सियाीााा (Bilazis)) सिकनीय (Biluria))
groups
Table 6: Target community based keywords.
Abusive
Sentiment
Sareasm
Vulgar
Model
Acc
M-F1
Acc
M-F1
Acc
M-FI
Acc
M-FI
BEBERRT
64.21
63.12
44.57
40.31
63.07
61.90
65.71
61.68
MuRII.
65.61
63.97
50.16
35.63
59.61
58.95
64.35
60.25
T
BanglaBERT
64.48
62.90
46.37
42.79
61.68
60.89
63.39
59.57
XLMR
66.18
65.07
49.71
43.21
59.21
58.97
61.83
58.34
VGGI6
67.86
65.70
47.98
40.96
63.71
62.30
72.74
67.06
ResNet-152
69.20
66.50
47.88
42.75
65.19
64.44
73.03
66.83
VIT
69.40
67.38
45.78
42.16
66.73
66.13
73.90
69.21
VAN
65.22
63.67
45.63
40.63
64.35
63.86
71.25
66.21
MU+RN(C)
69.08
66.32
45.93
41.53
64.97
64.20
71.87
66.49
XLM-RN(C)
68.88
66.64
47.66
42.97
64.18
63.07
73.80
67.91
MU+VIT(C)
68.73
67.06
47.26
42.76
65.69
65.29
72.22
68.37
XLM+VITIC)
70.02
67.83
47.51
43.88
66.83
66.18
74,20
69.28
T
CLIP(C)
72.81
70.51
49.02
45.59
69.05
68.28
77.49
71.66
+
MU+RN(L)
68.51
66.02
47.61
42.84
64.16
63.64
73.23
67.25
XLM+RN(L)
69.18
66.19
48.28
43.64
64.72
64.04
72.49
67.19
MU+VIT())
67.87
66.12
46.99
42.21
65.94
65.73
71.95
67.68
XIM--VI((L)
69.10
66.78
46.17
42.02
65.42
65.05
73.41
68.18
CLIP(L)
70.39
68.45
49.24
44.39
68.58
68.27
75.09
70.29
Table 7: Experimental results of different multi-task variants in unimodal and multimodal settings. The best performance in each column marked in bold, and the second best is underlined.
The VIT (Dosovitskiy et al., 2021) and VAN (Guo et al., 2022) models provided 768-dimensional and 512-dimensional feature vectors respectively for each meme, which were then passed through dense layers for prediction. We extracted 512- dimensional feature vectors for the meme text and image in the multimodal CLIP model. We fed them through two different fusion- based models discussed section 4. Further details about the other models can be found in section 4.1.
RAM and 16GB RTX 3080 GPU.
D More examples
In Figure 14, we have shown additional examples of abusive memes in contrast to the non-abusive ones.
E Multi-task classification
Since our dataset has multiple labels for each meme, we aim to understand if we can utilize a multi-task framework for abusive meme detection where vulgarity detection, sentiment analysis, and sarcasm detection act as secondary/auxiliarr tasks. To learn n number of tasks simultaneously. after extracting the features from the different models, these are passed through n = different linear
All the models run for 30 epochs with Adam optimizer, batch_sizz size 32, learning_rate rateeerrrtt_---eeeeeeee le 1. We store the results for the best validation macro- score. All the models are coded in Python using the Pytorch library. We use Ryzen 9, 5th gen 12 core processor, a Linux based system with 64GB

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layers. Each of these layers are responsible for making predictions for a different type of label. We employ cross-entropy as loss function train the network's parameters. As our primary task to classify memes as abusive or not, we assign loss weight for abusive classification 1, while for the other tasks, we assign loss weight of 0.5 each.
Table 7 presents the results of all the tasks we got in terms of accuracy and macro-F1 score. Here, we observe the following.
For the abusive meme detection, CLIP(C) (ace: 72.81, macro-F1: 70.51) achieved the highest performance, and CLIP(L))acc:: 70.39, macro-F1: 68.45) performed the second best.
Even for other labels also we observe CLIP(C) performs the best and CLIP(L) performs the second best (sentiment- CLIP(C): 45.59,
CLIP(L): 44.39; sarcasm- CLIP(C): 68.28,
CLIP(L): 68.27; vulgar- CLIP(C): 71.66,
CLIP(L): 70.29) in terms of macro F1 score. However, important to note that sentiment classification posed a challenge for all mod- cls, with their performance falling lower than expected.
Further, the comparison of these results with those in Table 2 reveals interesting in- sights. Some models showcase improvement, while others experience marginal performance degradation. Nonetheless, these shifts in per- formance not significant. Thus, the de- cision to pursue single-task multi-task approach can be tailored to specific task re- quirements.


Reviews:
[{"review_id": "aI8NhdqmUt", "reviewer": "Reviewer_xN2X", "summary": "", "questions": "Why did not authors perform multitasking as they have labels for  Sarcasm, Vulgar and Sentiment", "limitations": "", "rating": 2, "confidence": 5, "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 thank reviewer xN2X for providing us with insightful feedback. We are happy to clarify any further concerns.\n\nQ1: Multi-tasking results\n\nAns: Indeed, this was submitted to the \"Resources and Evaluation\" track since we felt that it was an important resource contribution to the abusive speech research community. However, as per the suggestion, we also performed multi-tasking and below is the table of the results we got in terms of Accuracy and Macro-F1 score for different labels. Here, we observe that for the abusive meme detection, CLIP(C) (Acc: 72.81, M-F1: 70.51) achieved the highest performance, and CLIP(L)(Acc: 70.39, M-F1: 68.45) performed the second-highest. Even for other labels also we observe CLIP(C) performs the best and CLIP(L) performs the second best (Sentiment: CLIP(C): 45.59, CLIP(L): 44.39 | Sarcasm: CLIP(C): 68.28, CLIP(L): 68.27 | Vulgar: CLIP(C): 71.66, CLIP(L): 70.29) in terms of Macro-F1 score. However, it is important to note that sentiment classification posed a challenge for all models, with their performance falling lower than expected. Further analysis, involving a comparison with the results of the Single Task Abusive Meme Detection (Table 2 in the manuscript), revealed interesting insights. Some models showcased improvement, while others experienced marginal performance degradation. Nonetheless, these shifts in performance were not significant. Thus, the decision to pursue a single-task or multi-task approach can be tailored according to specific project requirements. We shall add these new results in the revised version of the manuscript.\n\n| Model      | Abusive |       | Sentiment |       | Sarcasm |       | Vulgar |       |\n| ---------- | ------- | ----- | --------- | ----- | ------- | ----- | ------ | ----- |\n|            | Acc     | M-F1  | Acc       | M-F1  | Acc     | M-F1  | Acc    | M-F1  |\n| m-BERT     | 64.21   | 63.12 | 44.57     | 40.31 | 63.07   | 61.9  | 65.71  | 61.68 |\n| MuRIL      | 65.61   | 63.97 | 50.16     | 35.63 | 59.61   | 58.95 | 64.35  | 60.25 |\n| BanglaBERT | 64.48   | 62.9  | 46.37     | 42.79 | 61.68   | 60.89 | 63.39  | 59.57 |\n| XLMR       | 66.18   | 65.07 | 49.71     | 43.21 | 59.21   | 58.97 | 61.83  | 58.34 |\n| VGG16      | 67.86   | 65.7  | 47.98     | 40.96 | 63.71   | 62.3  | 72.74  | 67.06 |\n| ResNet-152 | 69.2    | 66.5  | 47.88     | 42.75 | 65.19   | 64.44 | 73.03  | 66.83 |\n| VIT        | 69.4    | 67.38 | 45.78     | 42.16 | 66.73   | 66.13 | 73.9   | 69.21 |\n| VAN        | 65.22   | 63.67 | 45.63     | 40.63 | 64.35   | 63.86 | 71.25  | 66.21 |\n| MU+RN(C)   | 69.08   | 66.32 | 45.93     | 41.53 | 64.97   | 64.2  | 71.87  | 66.49 |\n| XLM+RN(C)  | 68.88   | 66.64 | 47.66     | 42.97 | 64.18   | 63.07 | 73.8   | 67.91 |\n| MU+VIT(C)  | 68.73   | 67.06 | 47.26     | 42.76 | 65.69   | 65.29 | 72.22  | 68.37 |\n| XLM+VIT(C) | 70.02   | 67.83 | 47.51     | 43.88 | 66.83   | 6618  | 74.2   | 69.28 |\n| CLIP(C)    | 72.81   | 70.51 | 49.02     | 45.59 | 69.05   | 68.28 | 77.49  | 71.66 |\n| MU+RN(L)   | 68.51   | 66.02 | 47.61     | 42.84 | 64.16   | 63.64 | 73.23  | 67.25 |\n| XLM+RN(L)  | 69.18   | 66.19 | 48.28     | 43.64 | 64.72   | 64.04 | 72.49  | 67.19 |\n| MU+VIT(L)  | 67.87   | 66.12 | 46.99     | 42.21 | 65.94   | 65.73 | 71.95  | 67.68 |\n| XLM+VIT(L) | 69.1    | 66.78 | 46.17     | 42.02 | 65.42   | 65.05 | 73.41  | 68.18 |\n| CLIP(L)    | 70.39   | 68.45 | 49.24     | 44.39 | 68.58   | 68.27 | 75.09  | 70.29 |\n\nQ2: Error Analysis Reasoning\n\nAns: In Section 5.4, we conducted a thorough manual inspection of misclassified memes and detailed our observations. We illustrated these findings in Figure 13 by presenting examples of misclassified cases encompassing various scenarios.\nIn Figure 13(a), we encountered a scenario where the vision-based VIT model failed to capture the true essence of the meme. While the image seemingly portrays the Prime Minister of India, Narendra Modi, with Waterfall behind him, giving an impression of benign content; however, the textual component contains a derogatory term, 'Malaun,' directed toward  Narendra Modi. Based on this textual context, the text-based model successfully classified the meme as abusive.\nProceeding to Figure 13(b), an exciting case emerged where the XLMR model mispredicted the meme. The text lacked any abusive elements, yet the image overtly displayed sexual vulgarity toward women, and the VIT model labeled this as abusive.\nFigure 13(c) introduces another intriguing scenario. Analyzing the image and text in isolation would not suggest abusiveness. However, when considering both elements, the CLIP model accurately classified the meme as abusive and directed toward obese women.\nFigure 13(d) depicted a meme where the text and image conveyed offensiveness toward Pakistanis. The text contained the phrase \"Pakistan murdabad,\" which is inherently harmful for dissemination on social media. The accompanying image displayed individuals placing their feet on the Pakistani flag, a deeply disrespectful act. Consequently, all models correctly classified this as abusive.\nContinuing to Figure 13(e), an image portrayed a man seemingly experiencing a hangover. However, the meme insinuated that the former cricket coach, Ravi Shastri, is obsessed with an alcohol problem and is meant to bully Ravi Shastri. Interpreting such memes needs intricate reasoning abilities and contextual knowledge. We intend to incorporate this insightful explanation to strengthen the error analysis section in the final version of the paper.\n\nWe sincerely hope that you could re-evaluate this paper with goodwill.", "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": "The paper presents a good resource for multilingual multimodal toxic detection", "reasons_to_reject": "1. The paper is a resource paper and does not delve into any specific techniques to solve the task.\n2. Limited Novelty and contribution apart from the dataset.\n3. In section 5.4, the authors mention observations and fail to give proper reasoning.", "questions_to_authors": "Why did not authors perform multitasking as they have labels for  Sarcasm, Vulgar and Sentiment", "paper_topic_and_main_contributions": "The paper presents a Bengali meme dataset which consists of 4,043 Bengali memes.\nThey then performed benchmarking of this dataset for  hate classification using basic Multimodal techniques.", "excitement": 2, "reproducibility": 5, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "MU0u0HZ1qw", "reviewer": "Reviewer_UVRc", "summary": "", "questions": "", "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 thank reviewer UVRc for providing us with insightful feedback and are glad that the reviewer found our paper interesting. We are happy to clarify any further concerns.\n\nQ1: Deeper exploration of potential biases and challenges specific to the Bengali language and cultural nuances.\n\nAns: It is undoubtedly an exciting area to work in. The data incorporates at least two very large dialectal variations: (a) standard colloquial Bengali (spoken in West Bengal) and (b) Bangladeshi (spoken in East Bengal (Bangladesh)). The British colonizers partitioned these regions based on their socioeconomic structure and religion-based demography. The hate lexicons and the target communities vary based on these dialectal variations and are an additional challenge while handling the Bengali language. Based on the abusive meme's origin and the dialect in which it is spoken, the latent target can vary. Dialect-based meme annotation and subsequent performance analysis is an immediate future work. We shall add this discussion in the conclusion/limitation section of the paper. \n\nQ2: Comparative analysis with existing benchmarks for other languages\n\nAns: Although there has been limited research on multilingual abusive meme datasets, we have taken your suggestions into account. We have conducted a comparative analysis that includes our Bengali(bn) language-trained model and a multimodal Hindi(hi) code-mixed harmful/cyberbullying meme dataset (Maity, Krishanu, et al. \"A multitask framework for sentiment, emotion and sarcasm aware cyberbullying detection from multi-modal code-mixed memes.\"), as per your recommendation. Our experimentation focused exclusively on the CLIP(L) model, which exhibited superior performance among all the models considered.\nIn this experimental setup, we trained the model using the Bengali language and evaluated its performance using the Hindi language and vice versa. The subsequent performance comparison is outlined below. We observed that the model trained on the Hindi language exhibited poor performance when applied to Bengali, and conversely, the model trained on Bengali demonstrated subpar results when tested with Hindi. This outcome emphasizes the need to curate new abusive meme datasets for other low-resource languages. By doing so, we can accurately detect multilingual memes in low-resource languages and contribute to the advancement of research in this domain.\n\n| Transfer  | Acc   | M-F1  | F1(A) | P(A)  | R(A)  |\n| --------- | ----- | ----- | ----- | ----- | ----- |\n| hi - > hi | 71.64 | 71.46 | 73.77 | 75.2  | 72.4  |\n| hi-> bn   | 52.28 | 52.28 | 51.87 | 41.85 | 68.19 |\n| bn - > bn | 70.08 | 69.28 | 64.3  | 58.44 | 71.47 |\n| bn -> hi  | 50.46 | 45.07 | 27.86 | 70.44 | 17.36 |\n\nQ3: The paper could expand its scope by considering real-world deployment challenges and proposing strategies for addressing potential adversarial attacks.\n\nAns: Thank you for bringing up this matter. Indeed the abusive meme detection model can be very effective in content moderation especially when there is a surge in multimedia content over the social media platforms. The auto-discard technique, where a hateful content is automatically discarded from posting once flagged by an abuse detection model has become increasingly useful. Other approaches include alerting the user about a flagged post or lowering the visibility of the flagged post. Finally, moderators can be automatically informed to counter the flagged post so that it dilutes or nullifies the harmful effect of the post. In order to have such an effective flagging some of the challenges include continuous availability of training data, computational costs, and the imperative of upholding the model's sustained accuracy over time. We shall add this discussion in the revised version of the paper.", "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": "Introducing a new benchmark dataset for abusive meme detection in Bengali is a valuable and timely contribution, addressing a critical gap in research for low-resource languages. The paper systematically investigates the challenge of identifying harmful content on social media, particularly in the Bengali language, by developing and releasing the \"BanglaAbuseMeme\" dataset. By implementing and comparing various models, including text-based, image-based, and multimodal approaches, the paper demonstrates significant advancements in abusive meme detection, achieving a high macro F1-score. The qualitative error analysis further enriches the understanding of model performance and limitations. This research not only enhances our ability to curb online abuse but also paves the way for future studies in content moderation and NLP for underrepresented languages.", "reasons_to_reject": "While the introduction of a new benchmark dataset for abusive meme detection in Bengali holds promise, the paper's contributions might be limited by its focus on a single task within the broader context of content moderation. Additionally, although the multimodal model showcases improved performance, the paper could benefit from a deeper exploration of potential biases and challenges specific to the Bengali language and cultural nuances. The lack of comparative analysis with existing benchmarks for other languages might limit the broader impact of the study. Furthermore, the paper could expand its scope by considering real-world deployment challenges and proposing strategies for addressing potential adversarial attacks or evolving abusive content creation techniques.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper focuses on detecting abusive memes, which combine images and text to threaten or target individuals or communities online, with a specific emphasis on the Bengali language. The authors introduce the \"BanglaAbuseMeme\" dataset containing annotations for 4,043 Bengali memes, enabling the study of meme dynamics. They implement and compare baseline models for automatic abusive meme detection, including text-based, image-based, and multimodal approaches. The results demonstrate the superiority of multimodal models, with their best-performing model achieving a macro F1-score of 70.51. Through qualitative error analysis, the paper highlights model strengths and weaknesses in handling abusive memes, offering insights into the challenges posed by the Bengali language and cultural context. This work contributes to addressing online abuse and content moderation challenges, particularly in low-resource languages like Bengali.", "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": {}, "extra_text": {}}, {"review_id": "PXIv0BTRND", "reviewer": "Reviewer_zToY", "summary": "", "questions": "- Question A: What is the proportion of data that includes implicit abusive memes?\n- Question B: It would be helpful if you could provide some examples for the following scenarios: (1) memes that are vulgar but not abusive (Figure 5), (2) memes that have a positive sentiment but are abusive (Figure 6).\n- Question C: Table 3 indicates that, except for CLIP, concatenation performs better than late fusion across all methods. Can you provide an explanation for this observation?\n- Question D: In the first paragraph of Section 5.2, it is stated that \"the performance of the unimodal models varies across all target communities, while the multimodal model consistently reports good performance across all target communities.\" However, based on Figure 9, it seems that CLIP performs worse on the political target community. Could you clarify this discrepancy?", "limitations": "", "rating": 4, "confidence": 5, "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 thank reviewer zToY for providing us with detailed feedback and are really glad that the reviewer found our paper interesting. We are happy to clarify any further concerns.\n\nQ1: How the proposed methods compare to previous studies on abusive meme detection, specially on Bengali data. \n\nAns: There is no benchmark dataset publicly available on Bengali. Hence we could not perform this exercise. However there are datasets in other languages present performing similar work. We have conducted a comparative analysis that includes our Bengali(bn) language-trained model and a multimodal Hindi(hi) code-mixed harmful/cyberbullying meme dataset (Maity et al.,2022 \"A multitask framework for sentiment, emotion and sarcasm aware cyberbullying detection from multi-modal code-mixed memes.\"), as per your recommendation. Our experimentation focused exclusively on the CLIP(L) model, which exhibited superior performance among all the models considered.\nIn this experimental setup, we trained the model using the Bengali language and evaluated its performance using the Hindi language, and vice versa. The subsequent performance comparison is outlined below. We observed that the model trained on the Hindi language exhibited poor performance when applied to Bengali, and conversely, the model trained on Bengali demonstrated subpar results when tested with Hindi. This outcome emphasizes the need to curate new abusive meme datasets for other low-resource languages. By doing so, we can accurately detect multilingual memes in low-resource languages and contribute to the advancement of research in this domain.\n\n| Transfer  | Acc   | M-F1  | F1(A) | P(A)  | R(A)  |\n| --------- | ----- | ----- | ----- | ----- | ----- |\n| hi - > hi | 71.64 | 71.46 | 73.77 | 75.2  | 72.4  |\n| hi-> bn   | 52.28 | 52.28 | 51.87 | 41.85 | 68.19 |\n| bn - > bn | 70.08 | 69.28 | 64.3  | 58.44 | 71.47 |\n| bn -> hi  | 50.46 | 45.07 | 27.86 | 70.44 | 17.36 |\n\nQ2: Annotation strategy raises concerns, particularly with regards to discussing annotators' errors after every batch.\n\nAns: We followed the standard method for the annotation employed by other researchers who have undertaken similar tasks (Saha et al., 2021 \"“Short is the Road that Leads from Fear to Hate”: Fear Speech in Indian WhatsApp Groups.\", Pramanick et al., 2021 \"Detecting harmful memes and their targets.\"). These procedures help the annotators in gradually becoming proficient in the annotation task over time.\n\nQ3: Paper's figures and tables are not well-aligned with the main body\n\nAns:  We shall standardize these issues in the final version of the paper.\n\nQuestion A: What is the proportion of data that includes implicit abusive memes?\n\nThis is a very interesting question. We plan to address this in future work.\n\nQuestion B: It would be helpful if you could provide some examples for the following scenarios: (1) memes that are vulgar but not abusive (Figure 5), (2) memes that have a positive sentiment but are abusive (Figure 6).\n\nAns: Thank you for the suggestion. We will add these in the final version.\n\nQuestion C: Table 3 indicates that, except for CLIP, concatenation performs better than late fusion across all methods. Can you provide an explanation for this observation? \n\nAns: We believe that this depends on the combination of the dataset and task. In an earlier study by Das et al., 2022 ( \"Transfer Learning for Multilingual Abusive Meme Detection.\"), it has been shown for one language – concatenation performs better than late fusion and for another language the late fusion performed better. However the performance difference was minimal, similar to the results we have observed here. Even when we performed multitasking with additional labels, we observed CLIP(C) performs better (please see our response reviewer xN2X). Overall it can be said that the model’s performance may vary depending on the dataset or task we are performing. A better way to understand this would be to develop an explainable model; this is an important future research direction.\n\nQuestion D: In the first paragraph of Section 5.2, it is stated that \"the performance of the unimodal models varies across all target communities, while the multimodal model consistently reports good performance across all target communities.\" However, based on Figure 9, it seems that CLIP performs worse on the political target community. Could you clarify this discrepancy?\n\nAns: Yes for the political target community, the CLIP model's performance is comparatively subpar when contrasted with other models. This discrepancy might be attributed to the possibility that XLMR has been pretrained on data containing a substantial amount of political content, potentially contributing to its enhanced predictive capabilities within this category. A prior study conducted by Nicholls et al. in 2022 (\"Better political text classification using large language models.\") also supports the notion that XLMR outperforms in political text classification tasks. However, further research would be needed to establish this correlation.", "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": "- The paper introduces a new dataset to facilitate the task of detecting abusive memes in Bengali, a low-resource language. The dataset is planned to be made publicly available for research purposes, offering researchers a valuable resource for studying online abuse in this specific linguistic context. Moreover, the dataset includes additional labels that provide insights into sentiment, sarcasm, vulgarity, and target communities attacked, enhancing the richness of the data for further analysis.\n- The authors provide a thorough explanation of the annotation process, offering transparency and insights into how the dataset was labeled. The paper also showcases a comprehensive analysis of the data and its various label sets, providing valuable context and understanding of the characteristics of abusive memes in Bengali.\n- The paper conducts a thorough comparison of various baseline models, such as text-based, image-based, and multimodal approaches, for automatic detection of abusive memes.\n- The paper is well-written and easy to understand.", "reasons_to_reject": "- The paper does not clearly demonstrate how the proposed methods compare to previous studies on abusive meme detection, specially on Bengali data.\n- The annotation strategy raises concerns, particularly with regards to discussing annotators' errors after every batch. Given the subjective nature of the task, this approach may introduce bias and affect the reliability of the annotations.\n- The paper's figures and tables are not well-aligned with the main body, which affects the overall readability. Additionally, the small font size used in tables and figures can make it difficult for readers to comprehend the presented information.", "questions_to_authors": "- Question A: What is the proportion of data that includes implicit abusive memes?\n- Question B: It would be helpful if you could provide some examples for the following scenarios: (1) memes that are vulgar but not abusive (Figure 5), (2) memes that have a positive sentiment but are abusive (Figure 6).\n- Question C: Table 3 indicates that, except for CLIP, concatenation performs better than late fusion across all methods. Can you provide an explanation for this observation?\n- Question D: In the first paragraph of Section 5.2, it is stated that \"the performance of the unimodal models varies across all target communities, while the multimodal model consistently reports good performance across all target communities.\" However, based on Figure 9, it seems that CLIP performs worse on the political target community. Could you clarify this discrepancy?", "paper_topic_and_main_contributions": "The paper addresses the issue of online abuse through the creation and sharing of memes on social media platforms, with a particular focus on Bengali memes. It introduces a new dataset called BanglaAbuseMeme, comprising 4,043 Bengali memes annotated as abusive or non-abusive, with additional labels indicating sentiment, sarcasm, vulgarity, and target community attacked. Moreover, the authors implement and compare several baseline models for automatic detection of abusive Bengali memes, including text-based, image-based, and multimodal models. The results demonstrate that multimodal models, combining textual and visual information, outperform unimodal models. The paper concludes with a qualitative error analysis of misclassified memes by different models, providing insights into their respective strengths and weaknesses.", "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": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This study introduces a dataset comprising 4,043 memes labeled as either abusive or non-abusive. Additionally, benchmark results using various models are provided.

Reviewers zToY and UVRc both expressed appreciation for the dataset's contribution. Reviewer xN2X raised concerns about the multitask results, which the authors addressed in their rebuttal. The issue of "comparative analysis with existing benchmarks" raised by Reviewer UVRc was also tackled during the rebuttal.

To enhance the paper, please ensure all feedback from the reviewers is addressed.
Decision: Accept-Main