Crowd-sourced Explanations for Humorous Internet Memes

在許多的社群網站裡，幽默的圖片是很常見的。 但是，剛到這些網站的新手們通常都很難融入，因為這種次文化通常都有著一些隱涵的資訊。 在幽默圖片的種類裡，網路模因對於新手們是相對比較難理解的。 在這一篇論文裡，我們開發了一套系統可以藉由群眾外包的技術，來產生網路模因的解釋。 我們宣稱只要看過我們產生的解釋之後，就算對於網路模因不熟悉的人， 也都可以很快地了解其中的笑點。我們模板式的解釋可以突顯出笑話裡正常情況和笑點句子的不和諧。 這樣的解釋可以透過兩套人腦計算的流程來產生。 這裡的實驗結果顯示系統所產生的解釋可以很好的幫助新手來了解不熟悉的網路模因。 未來研究的方向可以考慮加入更多電腦計算的幫助，來改善計算人性這個領域的發展。Humorous images can be seen in many social media websites. However, newcomers to these websites often have trouble fitting in because the community subculture is usually implicit. Among all the types of humorous images, Internet memes are relatively hard for newcomers to understand. In this work, we develop a system that leverages crowdsourcing techniques to generate explanations for memes. We claim that people who are not familiar with Internet meme subculture can still quickly pick up the gist of the memes by reading the explanations. Our template-based explanations illustrate the incongruity between normal situations and the punchlines in jokes. The explanations can be produced by completing the two proposed human task processes. Experimental results suggest that the explanations produced by our system greatly help newcomers to understand unfamiliar memes. For further research, it is possible to employ our explanation generation system to improve computational humanities.Acknowledgments i Abstract iii List of Figures vii List of Tables x Chapter 1 Introduction 1 Chapter 2 Related Work 5 2.1 Computational Humor Recognition . . . . . . . . . . . . . . . . . . . 5 2.2 Crowdsourcing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 Linguistic Humor Theories . . . . . . . . . . . . . . . . . . . . . . . . 7 Chapter 3 Methodology 9 3.1 Template-Based Explanation . . . . . . . . . . . . . . . . . . . . . . . 3.2 Three Anti-Punchline Generation Methods . . . . . . . . . . . . . . . 11 3.2.1 Collection-Selection Process . . . . . . . . . . . . . . . . . . . 12 3.2.2 Direct Filling Process . . . . . . . . . . . . . . . . . . . . . . . 15 3.2.3 Expert Process . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Supporting Lines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Chapter 4 Experiments 19 4.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.2 Experiment Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Chapter 5 Discussion 5.1 28 Online Product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Chapter 6 Conclusion and Future Work 32 Bibliography 3