Popularity Prediction of Online Petitions using a Multimodal D eep R egression Model (2020)

Abstract: Online petitions offer a mechanism for peopleto initiate a request for change and gather sup-port from others to demonstrate support for thecause. In this work, we model the task of peti-tion popularity using both text and image rep-resentations across four different languages,and including petition metadata. We evaluateour proposed approach using a dataset of 75kpetitions from Avaaz.org, and find strong com-plementarity between text and images.

## introduction a petition is a formal request for change or an action to any authority, co-signed by a group of supporters @xcite . the targets of petitions are usually government agencies and business organizations. in this work we study petitions from avaaz.org, a popular petition platform available across six continents, with support for seventeen different languages. avaaz provides a platform for petitions and funding campaigns. an example petition is given in figure 1 , opposing the anti-counterfeiting trade agreement (acta) and supporting a free and open internet. the popularity of a petition, in terms of the number of signatures it attracts, is critical to its success, and predicting popularity can help petition organizers to enhance their engagement strategy by optimising the petition content. in particular in this work, we target the task of predicting petition popularity at the time of submission (independent of any social media or direct signature signal). while existing work on petitions has focused on their text content @xcite , images are also a key ingredient. 1 additionally, despite petitions being popular in many different languages, there has been no work on multilingual modeling. from a social science viewpoint, multilingual analysis can contribute to an understanding of issues present in different languages (or regions). previous research has shown that, other than petition content, metadata is also effective in modelling its popularity. @xcite showed that the category of a petition has an influence on its popularity and success, e.g., human trafficking related petitions get more signatures than health related ones. @xcite investigated the interaction between topics and geographic features, and showed that some issues receive broad geographic support (e.g., law & order, work & play) but others are far more local (e.g., animals & nature, driving). since we aim to model petition popularity across multiple countries, we study the utility of the author's country information. our contributions in this work are as follows: (1) we propose a multimodal regression approach for petition popularity prediction task using text and image features; (2) we experiment in both monoand multi-lingual data settings, to evaluate the impact of training data from other languages; and ## related work the majority of work on modeling petition popularity has focused on predicting popularity growth over time based on an initial popularity trajectory @xcite @xcite , e.g. given the number of signatures a petition gets in the first x hours, predicting the total number of signatures at the end of its lifetime. since the popularity of a petition also depends on its author's campaign strategies, @xcite and proskurnia et al. (2017) examined the impact of sharing petitions on twitter, as a time series regression task. however, none of this work analyzed the petition's content, which is a primary focus in this work, in addition to making the prediction at the time of submission rather than based on early social indicators. elnoshokaty et al. ( 2016 ) analyzed change.org petitions by performing correlation analysis of popularity against the petition's category, target goal set, and the distribution of words in general inquirer categories @xcite . @xcite is the closest work to this paper, which targets uk and us government petitions, and poses popularity prediction as a text regression task using a convolutional neural network model. though petition platforms such as avaaz.org and change.org are popular in different countries and languages, almost all the existing work has focused on the monolingual setting (almost exclusively on english). with the increasing use of petitions across the globe, it is necessary to model petitions across different languages @xcite . lastly, in addition to textual content, the choice of images and other multimodal information has been shown to have impact on the popularity of social media posts @xcite @xcite , but not utilized in the context of online petitions. ## dataset we use petitions from the avaaz.org dataset of @xcite , which consists of over 360k petitions in more than fifty languages. each petition is made up of its textual content, author details, country, count of shares on social media, language, and other metadata. for our work, we use the top four languages based on the raw count of petitions: ## conclusions we proposed an multimodal, multilingual approach to the task of petition popularity prediction, and found that while the text-only model was superior to the image-only model, the combination of multimodal features performed the best. exploring further choices of metadata, and alternate ways to model multilingual text is left to future work.