D iffus P oll: Conditional Text Diffusion Model for Poll Generation (2024)

Abstract: Online social media platforms often gather user feedback through polls to enhance user engagement. Automatically generating polls from social media and its context can decrease the labor expenses of media workers and enhance workplace productivity. However, on social media platforms, there are internet water armies that manipulate public opinion through sheer numbers and causing the comments to be biased, drowning out minority views. In such circumstances, polls created based on biased comments often have limited types of options and poor coverage. Therefore, it is crucial to diversify the poll options and try to listen to the voices of the minority. To achieve this, we introduce DiffusPoll, a novel paradigm for poll generation based on a non-autoregressive diffusion model that can generate diversified and high-quality samples. Under the new paradigm, we design a task-specific mask strategy tailored to the inherent logic of polls to optimize controlled generation. Furthermore, we also leverage additional attribute tags from comments to enhance the generation quality. Experimental results indicate that DiffusPoll has achieved state-of-the-art performance in both the quality and diversity of poll generation tasks, and is more likely to hit the voices of minority.

## introduction social media allows us to hear the public voices for a better understanding our society and making decisions. there are two traditional methods to gather the public voices. the first way is to collect all the comments from each user, which would be labor-intensive and inefficient when the information increases. the second way is to only collect representative and valuable information, such as the "like" or the "reply". compared to expressing opinions themselves directly, "voting" is an kfc has three burgers i like, and they are constantly innovating. mcdonald's offers great value for the price. however, i prefer eating wallace. why is there no pizza hut? kfc's chicken wrap is unbeatable. i like burger king's fries and kfc's burgers. ## diffuspoll the diffuspoll's framework is illustrated in figure 2 and we will introduce our framework in three parts. first, we develop the attribute tags control (section 3.1), which aims to enhance the quality and coverage of polls by mining the potential options from the users' comments. second, we design a task-specific mask strategy (section 3.2) to address the issue of out-of-order sentences generated by vanilla diffusion language models. third, we employ the diffusion component (section 3.3) to generate polls by decoding from a rich semantic latent state and utilize our diversity loss to balance diversity and coverage. in this paper, we treat the poll generation as sequence-to-sequence text generation tasks. formally, given a m-length source post with contextual comments w @xmath0, ..., ω u m } and c-length attribute tags extracted from comments as conditional control w @xmath1, ..., ω c c }, we aim to develop a text diffusion model that can generate a n-length poll w @xmath2, ..., ω y n }, conditioned on the input post, context, and attribute tags. ## case studies here we list a case where different models generated problems based on the same scenario shown in figure 5 . the green part represents the fidelity of the generated sample to the golden input, the orange part represents comments or options from the minority, and the blue represents the majority. firstly, comparing the first two transformerbased models, unipoll and t5-pegasus, we can find that the polls they generate are shorter and have fewer and more concise options. even the results of unipoll are the same as those of t5, indicating that the ability of unipoll comes from the pre-training of t5 itself. besides, we follow the work @xcite , also compared the performance of the large language model on the same scenario. we choose the chatgpt in gpt-3.5 and gpt-4 models to generate the poll by the prompt. the results clearly show that these large models generate more poll options, but the generated poll questions are pretty different from golden samples, which means the gpt will give the poll less loyalty and relevance. looking carefully at these option candidate sets, we can find that the options generated by these gpt models are closer to the keyword from comments. we believe that this phenomenon comes from gpt's training data containing keyword extraction and summary tasks. however, when the comment section is occupied by the internet water army, and most comments become dominant, the option of gpt to generate a poll runs the risk of being controlled by the internet water army. moreover, we found that our diffuspoll generates poll not only effectively maintained loyalty but also made breakthroughs in the diversity of options. an interesting phenomenon is that our model also learned the option "others...", which shows that the model's generated poll generation is scalable and more suitable for real scenarios. ## conclusion in this paper, we use the diffusion model to tackle the poll generation for generating high-quality and diverse samples. we introduce the diffuspoll, a conditional diffusion model, where the conditional parts use the task-specific mask strategy and attribute tags to improve performance and diversity. experimental results show that diffuspoll matches the performance of the transformer model while offering greater diversity, which makes it promising for application in real social media to enhance user engagement and mine voices of the minority.