Automatic Discovery of Political Meme Genres with Diverse Appearances

Forms of human communication are not static -- we expect some evolution in the way information is conveyed over time because of advances in technology. One example of this phenomenon is the image-based meme, which has emerged as a dominant form of political messaging in the past decade. While originally used to spread jokes on social media, memes are now having an outsized impact on public perception of world events. A significant challenge in automatic meme analysis has been the development of a strategy to match memes from within a single genre when the appearances of the images vary. Such variation is especially common in memes exhibiting mimicry. For example, when voters perform a common hand gesture to signal their support for a candidate. In this paper we introduce a scalable automated visual recognition pipeline for discovering political meme genres of diverse appearance. This pipeline can ingest meme images from a social network, apply computer vision-based techniques to extract local features and index new images into a database, and then organize the memes into related genres. To validate this approach, we perform a large case study on the 2019 Indonesian Presidential Election using a new dataset of over two million images collected from Twitter and Instagram. Results show that this approach can discover new meme genres with visually diverse images that share common stylistic elements, paving the way forward for further work in semantic analysis and content attribution.Comment: 13 pages, 14 figure

LT3 at SemEval-2020 Task 8 : multi-modal multi-task learning for memotion analysis

Internet memes have become a very popular mode of expression on social media networks today. Their multi-modal nature, caused by a mixture of text and image, makes them a very challenging research object for automatic analysis. In this paper, we describe our contribution to the SemEval2020 Memotion Analysis Task. We propose a Multi-Modal Multi-Task learning system, which incorporates “memebeddings”, viz. joint text and vision features, to learn and optimize for all three Memotion subtasks simultaneously. The experimental results show that the proposed system constantly outperforms the competition’s baseline, and the system setup with continual learning (where tasks are trained sequentially) obtains the best classification F1-scores

Netizens’ criticism of the government’s policy of “Meme Lockdown” during the Covid-19 pandemic; in Indonesia

Indonesia was shocked by the presence of the Corona-19 virus in early 2020. Indonesian people respond to policies related to handling Covid-19 by closing access to their territory and making memes about corona. One of the interesting phenomena that occurred during the Covid-19 pandemic was the number of banners or memes posted in the alleys of human settlements in Indonesia, as a form of freedom of opinion to respond to the policies of the Indonesian Government Program in preventing the more massive spread of Covid-19. This study uses a qualitative descriptive method with the data used in this study is a language game on photo uploads in the form of memes on Instagram accounts. The selected data is adjusted to the research needs and is representative data. The purpose of this study is to describe language games with sound and semantic substitution in the Lockdown Policy Meme on the Covid-19 pandemic in Indonesia through Instagram. The results showed that in the field of phonology tended to use substitution language games, while in the field of semantics, the most widely used was homonym language games. The language game in memes during the Covid-19 Pandemic has not yet become a force affecting the policies implemented by the Indonesian government. In other words, the anxiety and uncertainty were hidden in the Corona meme only meant as a pun or humor that can make the reader smile a little and feel optimistic. This paper has implications for developing criticism of government policies via the internet as a medium of communication and for managing the balance between stability and change due to the Covid-19 pandemic in Indonesia. This paper fulfils an identified need to study how the internet as public sphere and medium to communicate about government policies in the current era

Online media literacy intervention in Indonesia reduces misinformation sharing intention

Media literacy is widely viewed as an important tool in the fight against the spread of misinformation online. However, efforts to boost media literacy have primarily focused on Western-media and Western-oriented social media platforms, which are substantively different from the media and platforms used widely in the Global South. In the present work, we focus on the media ecosystem of Indonesia and report the results of an online media literacy intervention consisting of short-videos that were targeted specifically to social media users in Indonesia (N= 656). We found that participants in our media literacy intervention were 64% more likely to reduce their sharing intentions of false headlines than our control group (p \u3c 0.001). Our novel media literacy intervention shows promise as a useful tool to reduce misinformation in Southeast Asia