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A Dataset for Troll Classification of TamilMemes

Social media are interactive platforms that facilitate the creation or sharing of information, ideas or other forms of expression among people. This exchange is not free from offensive, trolling or malicious contents targeting users or communities. One way of trolling is by making memes, which in most cases combines an image with a concept or catchphrase. The challenge of dealing with memes is that they are region-specific and their meaning is often obscured in humour or sarcasm. To facilitate the computational modelling of trolling in the memes for Indian languages, we created a meme dataset for Tamil (TamilMemes). We annotated and released the dataset containing suspected trolls and not-troll memes. In this paper, we use the a image classification to address the difficulties involved in the classification of troll memes with the existing methods. We found that the identification of a troll meme with such an image classifier is not feasible which has been corroborated with precision, recall and F1-score.


Introduction
Traditional media content distribution channels such as television, radio or newspapers are monitored and scrutinized for their content. Nevertheless, social media platforms on the Internet opened the door for people to contribute, leave a comment on existing content without any moderation. Although most of the time, the internet users are harmless, some produce offensive content due to anonymity and freedom provided by social networks. Due to this freedom, people are becoming creative in their jokes by making memes. Although memes are meant to be humorous, sometimes it becomes threatening and offensive to specific people or community. On the Internet, a troll is a person who upsets or starts a hatred towards people or community. Trolling is the activity of posting a message via social media that is intended to be offensive, provocative, or menacing to distract which often has a digressive or off-topic content with the intent of provoking the audience (Bishop, 2013;Bishop, 2014; Mojica de la Vega and Ng, 2018;Suryawanshi et al., 2020). Despite this growing body of research in natural language processing, identifying trolling in memes has yet to be investigated. One way to understand how meme varies from other image posts was studied by Wang and Wen (2015). According to the authors, memes combine two images or are a combination of an image and a witty, catchy or sarcastic text. In this work, we treat this task as an image classification problem. Due to the large population in India, the issue has emerged in the context of recent events. There have been several threats towards people or communities from memes. This is a serious threat which shames people or spreads hatred towards people or a particular community (Kumar et al., 2018;Rani et al., 2020;Suryawanshi et al., 2020). There have been several studies on moderating trolling, however, for a social media administrator memes are hard to monitor as they are region-specific. Furthermore, their meaning is often obscure due to fused image-text representation. The content in Indian memes might be written in English, in a native language (native or foreign script), or in a mixture of languages and scripts (Ranjan et al., 2016;Chakravarthi et al., 2018;Jose et al., 2020;Priyadharshini et al., 2020;Chakravarthi et al., 2020a;Chakravarthi et al., 2020b). This adds another challenge to the meme classification problem. In Figure 1, Example 1 is written in Tamil with two images and Example 2 is written in English and Tamil (Roman Script) with two images. In the first example, the meme is trolling about the "Vim dis-washer" soap. The information in Example 1 can be translated into English as "the price of a lemon is five Rupees", whereby the image below shows a crying person. Just after the crying person the text says "The price of a Vim bar with the power of 100 Lemon is just 10 Rupees". This is an example of opinion manipulation with trolling as it influences the user opinion about products, companies and politics. This kind of memes might be effective in two ways. On the one hand, it is easy for companies and political parties to gain popularity. On the other hand, the trolls can damage the reputation of the company name or political party name. Example 2 shows a funny meme; it shows that a guy is talking to a poor lady while the girl in the car is looking at them. The image below includes a popular Tamil comedy actor with a short text written beneath "We also talk nicely to ladies to get into a relationship". Even though there is a widespread culture of memes on the Internet, the research on the classification of memes is not studied well. There are no systematic studies on classifying memes in a troll or not-troll category. In this work, we describe a dataset for classifying memes in such categories. To do this, we have collected a set of original memes from volunteers. We present baseline results using convolutional neural network (CNN) approaches for image classification. We report our findings in precision, recall and F-score and publish the code for this work at https: //github.com/sharduls007/TamilMemes.

Troll Meme
A troll meme is an implicit image that intents to demean or offend an individual on the Internet. Based on the definition "Trolling is the activity of posting a message via social media that tend to be offensive, provocative, or menacing (Bishop, 2013;Bishop, 2014; Mojica de la Vega and Ng, 2018)". Their main function is to distract the audience with the intent of provoking them. We define troll memes as a meme, which contains offensive text and non-offensive images, offensive images with non-offensive text, sarcastically offensive text with non-offensive images, or sarcastic images with offensive text to provoke, distract, and has a digressive or off-topic content with intend to demean or offend particular people, group or race. Figure 2 shows examples of trolling memes, Example 3 is trolling the potato chip brand called Lays. The translation of the text is "If you buy one packet of air, then 5 chips free", with its intention to damage the company's reputation. Figure 2 illustrates examples of not-troll memes. The translation of Example 4 would be "Sorry my friend (girl)". As this example does not contain any provoking or offensive content and is even funny, it should be listed in the not-troll category. As a troll meme is directed towards someone, it is easy to find such content in the comments section or group chat of social media. For our work, we collected memes from volunteers who sent them through WhatsApp, a social media for chatting and creating a group chat. The suspected troll memes then have been verified and annotated manually by the annotators. As the users who sent these troll memes belong to the Tamil speaking population, all the troll memes are in Tamil. The general format of the meme is the image and Tamil text embedded within the image. Most of the troll memes comes from the state of Tamil Nadu, in India. The Tamil language, which has 75 million speakers, 1 belongs to the Dravidian language family (Rao and Lalitha Devi, 2013;Chakravarthi et al., 2019a;Chakravarthi et al., 2019b;Chakravarthi et al., 2019c) and is one of the 22 scheduled languages of India (Dash et al., 2015). As these troll memes can have a negative psychological effect on an individual, a constraint has to be in place for such a conversation. In this work, we are attempting to identify such troll memes by providing a dataset and image classifier to identify these memes.

Related Work
Trolling in social media for text has been studied extensively (Bishop, 2013;Bishop, 2014;Mojica de la Vega and Ng, 2018;Malmasi and Zampieri, 2017;Kumar et al., 2018;Kumar, 2019). Opinion manipulation trolling (Mihaylov et al., 2015b;Mihaylov et al., 2015a), troll comments in News Community (Mihaylov and Nakov, 2016), and the role of political trolls (Atanasov et al., 2019) have been studied. All these considered the trolling on text-only media. However, meme consist of images or images with text.
A related research area is on offensive content detection. Various works in the recent years have investigated Offensive and Aggression content in text (Clarke and Grieve, 2017;Mathur et al., 2018;Nogueira dos Santos et al., 2018;Galery et al., 2018). For images, Gandhi et al. (2019) deals with offensive images and non-compliant logos. They have developed a computer-vision driven offensive and non-compliant image detection algorithm that identifies the offensive content in the image. They have categorized images as offensive if it has nudity, sexually explicit content, abusive text, objects used to promote violence or racially inappropriate content. The classifier takes advantage of a pre-trained object detector to identify the type of object in the image and then sends the image to the unit which specializes in detecting objects in the image. The majority of memes do not contain nudity or explicit sexual content due to the moderation of social media on nudity. Hence unlike their research, we are trying to identify troll memes by using image features derived by use of a convolutional neural network. Hate speech is a subset of offensive language and datasets associated with hate speech have been collected from social media such as Twitter (Xiang et al., 2012), Instagram (Hosseinmardi et al., 2015), Yahoo (Nobata et al., 2016), YouTube (Dinakar et al., 2012). In all of these works, only text corpora have been used to detect trolling, offensive, aggression and hate speech. Nevertheless, for memes, there is no such dataset. For Indian language memes, it is not available as to our knowledge. We are the first to develop a meme dataset for Tamil, with troll or not-troll annotation.

Ethics
For our study, people provided memes voluntarily for our research. Additionally, all personal identifiable information such as usernames are deleted from this dataset. The annotators were warned about the trolling content before viewing the meme, and our instructions informed them that they could quit the annotation campaign anytime if they felt uncomfortable.

Data collection
To retrieve high-quality meme data that would likely to include trolling, we asked the volunteers to provide us with memes that they get in their social media platforms, like WhatsApp, Facebook, Instagram, and Pinterest. The data was collected between November 1, 2019, until January 15, 2019, from sixteen volunteers. We are not disclosing any personal information of the volunteers such as gender as per their will. Figure 3 shows an example of the collected memes. We removed duplicate memes, however, we kept memes that uses the same image but different text. This was a challenging task since the same meme could have different file names. Hence the same meme could be annotated by different annotators. Due to this, we checked manually and removed such duplicates before sending them to annotators. An example is shown in Figure 3, where the same image with different text is used. Example 5 describes the image as "can not understand what you are saying", whereby Example 6 describes image as "I am confused".

Annotation
After we obtained the memes, we presented this data to the annotators using Google Forms. To not over-burden the annotators, we provided ten memes per page and hundred memes per form. For each form, the annotators are asked to decide if a given meme is of category troll or not-troll. As a part of annotation guidelines, we gave multiple examples of troll memes and not-troll memes to the annotators. The annotation for these examples has been done by the an annotator who is considered as a expert as well as a native Tamil speaker. Each meme is assigned to two different annotators, a male and a female annotator. To ensure the quality of the annotations and due to the region-specific nature of the annotation task, only native speakers from Tamil Nadu, India were recruited as annotators. Although we are not disclosing the gender demographics of volunteers who provided memes, we have gender-balanced annotation since each meme has been annotated by a male and a female.A meme is considered as troll only when both of the annotators label it as a troll.

Inter-Annotator Agreement
In order to evaluate the reliability of the annotation and their robustness across experiments, we analyzed the interannotator agreement using Cohen's kappa (Cohen, 1960). It compares the probability of two annotators agreeing by chance with the observed agreement. It measures agreement expected by chance by modelling each annotator with separate distribution governing their likelihood of assigning a particular category. Mathematically, where K is the kappa value, p(A) is the probability of the actual outcome and p(E) is the probability of the expected outcome as predicted by chance (Bloodgood and Grothendieck, 2013). We got a kappa value of 0.62 between two annotators (gender balance male and female annotators). Based on Landis and Koch (1977) and given the inherent obscure nature of memes, we got fair agreement amongst the annotators.

Data Statistics
We collected 2,969 memes, of which most are images with text embedded on them. After the annotation, we learned that the majority (1,951) of these were annotated as troll memes, and 1,018 as not-troll memes. Furthermore, we observed that memes, which have more than one image have a high probability of being a troll, whereas those with only one image are likely to be not-troll. We included Flickr30K 2 images (Young et al., 2014) to the not-troll category to address the class imbalance. Flickr30K is only added to training, while the test set is randomly chosen from our dataset. In all our experiments the test set remains the same.

Methodology
To demonstrate how the given dataset can be used to classify troll memes, we defined two experiments with four variations of each. We measured the performance of the proposed baselines by using precision, recall and F1-score for each class, i.e. "troll and not-troll". We used ResNet (He et al., 2016) and MobileNet (Howard et al., 2017) as a baseline to perform the experiments. We give insights into their architecture and design choices in the sections below.

ResNet
ResNet has won the ImageNet ILSVRC 2015 (Russakovsky et al., 2015) classification task. It is still a popular method for classifying images and uses residual learning which connects low-level and high-level representation directly by skipping the connections in-between. This improves the performance of ResNet by diminishing the problem of vanishing gradient descent. It assumes that a deeper network should not produce higher training error than a shallow network. In this experiment, we used the ResNet architecture with 176 layers. As it was trained on the ImageNet task, we removed the classification (last) layer and used GlobalAveragePooling in place of fully connected layer to save the computational cost. Later, we added four fully connected layers with the classification layer which has a sigmoid activation function.This architecture is trained with or without pre-trained ImageNet weights.

MobileNet
We trained MobileNet with and without ImageNet weights. The model has a depth multiplier of 1.4, and an input dimension of 224×224 pixels. This provides a 1, 280×1.4 = 2 https://github.com/BryanPlummer/flickr30k entities 1, 792 -dimensional representation of an image, which is then passed through a single hidden layer of a dimensionality of 1, 024 with ReLU activation, before being passed to a hidden layer with input dimension of (512,None) without any activation to provide the final representation h p . The main purpose of MobileNet is to optimize convolutional neural networks for mobile and embedded vision applications. It is less complex than ResNet in terms of number of hyperparameters and operations. It uses a different convolutional layer for each channel, this allows parallel computation on each channel which is Depthwise Separable Convolution. Later on the features extracted from these layers have been combined using the pointwise convolution layer. We used MobileNet to reduce the computational cost and compare it with the computationally intensive ResNet.

Experiments
We experimented with ResNet and MobileNet. The variation in experiments comes in terms of the data on which the models have been trained on, while the test set (300 memes) remained the same for all experiments. In the first variation, TamilMemes in Table 1, we trained the ResNet and Mo-bileNet models on our Tamil meme dataset(2,669 memes). The second variation, i.e. TamilMemes + ImageNet uses pre-trained ImageNet weights on the Tamil memes dataset.
To address the class imbalance, we added 1,000 images from the Flickr30k dataset to the training set in the third variation i.e. TamilMemes + ImageNet + Flickr1k. As a result, the third variation has 3,969 images (1,951 trolls and 2,018 not-trolls). In the last variation, TamilMemes + ImageNet + Flickr30k, we added 30,000 images from the Flickr30k dataset to not-troll category. Flickr dataset has images and the captions which describes the image. We used these images as a not-troll category because they do not convey trollings without the context of the text. Except for the TamilMemes baseline, we are using pre-trained Im-ageNet weights for all other variations. Images from the Flickr30k dataset are used to balance the not-troll class in the TamilMemes + ImageNet + Flickr1k variation. On the one hand, the use of all the samples from the Flickr30k dataset as not-troll in the fourth variation introduces the class imbalance by significantly increasing the number of not-troll samples compared to the troll one. On the other hand, in the first variation, a higher number of troll meme samples again introduces a class imbalance.

Result and Discussion
In the ResNet variations, we observed that there is no change in the macro averaged precision, recall and F1-score except for TamilMemes  hand, we see improvement in precision at detecting trolls in the TamilMeme + ImageNet variation. This shows that MobileNet can leverage transfer learning to improve results. The relatively poor performance of MobileNet on the TamilMeme variation shows that it can not learn complex features like ResNet does to identify troll memes. For ResNet, the trend in the macro averaged score can be seen increasing in TamilMemes + ImageNet and TamilMemes + ImageNet + Flickr1k variations when compared to the TamilMemes variation. The TamilMemes + ImageNet + Flickr30k variation shows a lower macro averaged score than that of the TamilMemes + ImageNet + Flickr1k variation in both MobileNet and ResNet. Overall the precision for troll class identification lies in the range of 0.28 and 0.37, which is rather less than that of the not-troll class which lies in the range of 0.64 and 0.68. When we train ResNet in class imbalanced data in TamilMemes and TamilMemes + ImageNet + Flickr30k variations, results shows that the macro-averaged score of these variations are not hampered by the class imbalance issue. While for same variations MobileNet shows poor macro-averaged precision and recall score when compared with other variations. This shows that MobileNet is more susceptible to class imbalance issue than ResNet.

Conclusions and Future work
As shown in the Table 1 the classification model performs poorly at identifying of troll memes. We observed that this stems from the problem characteristics of memes. The meme dataset is unbalanced and memes have both image and text embedded to it with code-mixing in different forms. Therefore, it is inherently more challenging to train a classifier using just images. Further, the same image can be used with different text to mean different things, potentially making the task more complicated.
To reduce the burden placed on annotators, we plan to use a semi-supervised approach to the size of the dataset. Semisupervised approaches have been proven to be of good use to increase the size of the datasets for under-resourced scenarios. We plan to use optical character recognizer (OCR) followed by a manual evaluation to obtain the text in the images. Since Tamil memes have code-mixing phenomenon, we plan to tackle the problem accordingly. With text identification using OCR, we will be able to approach the problem in a multi-modal way. We have created a meme dataset only for Tamil, but we plan to extend this to other languages as well.