This dataset presents 24 studies on Internet memes published between 2015-2023 on the Taylor and Francis Online database.

This dataset collects one-year Reddit posts, post metadata, post title sentiments, and post comments threads from subreddit: r/GME, r/superstonk, r/DDintoGME, and r/GMEJungle.

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Memes Data 2

## Overview

Memes Data 2 is a dataset for object detection tasks - it contains Memes Zt1p annotations for 998 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

ABSTRACT In recente years, it has been assumed academically that memes published on Facebook and other social networking sites (SRS) are textual/ discursive genres (cf. PASSOS, 2012; ; SILVA, 2016; GUERRA; BOTTA, 2018), thesis defended with different criteria and different genre theories. The objective of this paper is to discuss the generic status of memes on social networking sites. In order to achieve the purpose, I brought the studies of Bakhtin (2009; 2011) and , to discuss the concept of genre; and in Dawkins (2010), Blackmore (2000), Knobel and Lankshear (2005; 2007) and Cavalcante and Oliveira (2019), who discuss the nature of the meme. Methodologically, I analyze seven statements socially recognized as memes, which were published on Facebook in the last five years. The criteria for this were viralization and remixability, in its constitution. The results suggest that, under the label of meme, in fact, there are different genres, such as commercials and institucional ads, comic strips and serial comic strips, criticisms, reminders and motivational messages, which leads to question the generic status of what is recognized sociocognitively as meme.

Dataset

This dataset was created by Siya Garg

Contents

The DANKMEMES Dataset is composed of 2,361 images, half memes and half not, automatically extracted from Instagram through a Python script aimed at the hashtag related to the Italian government crisis (“#crisidigoverno”). It was created and used in the context of the DankMemes (https://dankmemes2020.fileli.unipi.it), a shared task proposed for the 2020 EVALITA campaign (http://www.evalita.it/2020), focusing on the automatic classification of In- ternet memes. The task encompasses three subtasks, aimed at: detecting memes (Task A), detecting the hate speech in memes (Task B) and clustering memes according to events (Task C).

The dataset is split into training and test sets, in a proportion of 80-20% of items. The test dataset has been provided without gold labels, provided in a separate file for each subtask.

For each subtask, the dataset consists of:

a folder with images in .jpg format

- a .csv file with the associated image embeddigs, computed employing ResNet (He et al., 2016), a state-of-the-art model for image recognition based on Deep Residual Learning.

- a .csv file with the associated variables

The variables provided are:

- File: the name of the image file associated with the variables;

- Engagement: the number of comments and likes of the image;

- Date: when the image has first been posted on Instagram;

- Picture manipulation: entails the degree of visual modification of the images. Non-manipulated or low impact changes are labeled 0 (e.g. addition of text, or logo). Heavily manipulated, impactful changes (e.g. images altered to include political actors) are labeled 1;

- Visual actors: the political actors (i.e. politicians, parties’ logos) portrayed visually, as edited into the picture or portrayed in the original image;

- Text: the textual content of the image has been extracted through optical character recognition (OCR) using Google’s Tesseract-OCR Engine, and further manually corrected;

- (for task A) Meme: binary feature, where 0 represents non meme images and 1 meme images.

- (for task B) Hate speech: binary feature only for memes. It differentiates memes with offensive language (1) from non offensive memes (0).

- (for task C) Event: feature only for meme images, categorizing them according to 4 events related to the 2019 Italian government crisis

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Inc. (Tokyo

No description was included in this Dataset collected from the OSF

Supplemental data for meme culture. Visit https://dataone.org/datasets/sha256%3Ad0476e1397f713dc236045a6b113010422c305ab906fff930bab0d70cc35df43 for complete metadata about this dataset.

The dramatic increase in the use of social media platforms for information sharing has also driven a significant rise in online abuse. A simple yet effective method of targeting individuals or communities involves creating memes, often combining an image with a brief piece of text overlaid on top. These harmful elements are widely used and pose a threat to online safety. Therefore, developing efficient models for detecting and flagging abusive memes is essential. This challenge becomes even more demanding in a low-resource setting, such as Bengali memes (i.e., images with Bengali text embedded in them), due to the absence of benchmark datasets on which AI models can be trained. This work addresses this gap by creating a Bengali meme dataset of 4K data points.

This upload includes the labeled Bengali meme dataset obtained from the web, described in the paper 'BanglaAbuseMeme: A Dataset for Bengali Abusive Meme Classification.'

Details of Motor Vehicle Collisions in New York City provided by the Police Department (NYPD).

Meme Template Generator App Market Outlook

According to our latest research, the global meme template generator app market size is valued at USD 1.32 billion in 2024 and is projected to reach USD 4.19 billion by 2033, growing at a robust CAGR of 13.7% during the forecast period. This market’s impressive growth is driven by the explosive popularity of meme culture across social media platforms, coupled with the increasing demand for user-friendly and customizable content creation tools.

One of the primary growth factors for the meme template generator app market is the rapid expansion of social media usage globally. Platforms like Instagram, Twitter, Facebook, and TikTok have become breeding grounds for viral content, with memes serving as a universal language for humor, commentary, and communication. The proliferation of smartphones and high-speed internet has empowered users to create, share, and consume memes at an unprecedented rate. This trend is further amplified by the younger demographic, particularly Gen Z and Millennials, who are both prolific consumers and creators of meme content, thereby fueling the demand for intuitive and feature-rich meme template generator apps.

Another significant driver is the increasing adoption of meme-based marketing strategies by brands and enterprises. Businesses are leveraging memes to engage with audiences in a relatable and humorous manner, enhancing brand recall and driving social media engagement. Meme template generator apps are now equipped with advanced editing features, AI-driven recommendations, and collaboration tools, enabling content creators and marketing teams to quickly produce high-impact visual content. The commercial use segment is witnessing substantial growth as enterprises seek innovative ways to connect with digital-native consumers, making meme generation an integral part of their content marketing arsenal.

Furthermore, the integration of cloud-based technologies and artificial intelligence is revolutionizing the meme template generator app market. Cloud-based deployment allows users to access and edit meme templates from any device, ensuring seamless collaboration and real-time updates. AI-powered features such as automated caption suggestions, template recommendations, and image recognition are making meme creation more accessible to users with varying levels of technical expertise. These technological advancements are lowering entry barriers, expanding the user base, and fostering continuous innovation within the market.

From a regional perspective, North America currently leads the meme template generator app market, accounting for the largest revenue share in 2024, followed closely by Europe and Asia Pacific. The United States, in particular, is a hotspot for meme culture, driven by a tech-savvy population and a vibrant digital content ecosystem. However, Asia Pacific is expected to witness the highest CAGR during the forecast period, propelled by the rapid adoption of smartphones, burgeoning youth population, and the rising influence of local social media platforms. Latin America and the Middle East & Africa are also emerging as promising markets, driven by increasing internet penetration and cultural shifts toward digital expression.

Platform Analysis

The platform segment of the meme template generator app market is categorized into iOS, Android, and web-based solutions. The iOS platform continues to enjoy significant popularity, particularly in markets such as North America and Western Europe, where Apple devices have a substantial user base. iOS-based meme template generator apps are often distinguished by their sleek user interfaces, robust security features, and seamless integration with other Apple services. Developers targeting the iOS ecosystem benefit from a loyal customer base that is willing to pay for premium features and ad-free experiences, contributing to higher average revenue per user (ARPU) compared to other platforms.

Evolutionary approaches to culture remain contentious. A source of contention is that cultural mutation may be substantial and, if it drives cultural change, then current evolutionary models are not adequate. But we lack studies quantifying the contribution of mutations to directional cultural change. We estimated the contribution of one type of cultural mutations – modification of memes - to directional cultural change using an amenable study system: learned birdsongs in a species that recently entered an urban habitat. Many songbirds have higher minimum song frequency in cities, to alleviate masking by low-frequency noise. We estimated that the input of meme modifications in an urban songbird population explains about half the extent of the population divergence in song frequency. This contribution of cultural mutations is large, but insufficient to explain the entire population divergence. The remaining divergence is due to selection of memes or creation of new memes. We conclude tha...

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The files contain the data set of research articles reviewed for the study in a seperate word file.The files also contain two figures and one table, all included in one word file.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Based on professional technical analysis and AI models, deliver precise price‑prediction data for BOOK OF MEME on 2025-08-02. Includes multi‑scenario analysis (bullish, baseline, bearish), risk assessment, technical‑indicator insights and market‑trend forecasts to help investors make informed trading decisions and craft sound investment strategies.