P oli WAM : An Exploration of a Large Scale Corpus of Political Discussions on W hats A pp Messenger (2021)

Abstract: WhatsApp Messenger is one of the most popular channels for spreading information with a current reach of more than 180 countries and 2 billion people. Its widespread usage has made it one of the most popular media for information propagation among the masses during any socially engaging event. In the recent past, several countries have witnessed its effectiveness and influence in political and social campaigns. We observe a high surge in information and propaganda flow during election campaigning. In this paper, we explore a high-quality large-scale user-generated dataset curated from WhatsApp comprising of 281 groups, 31,078 unique users, and 223,404 messages shared before, during, and after the Indian General Elections 2019, encompassing all major Indian political parties and leaders. In addition to the raw noisy user-generated data, we present a fine-grained annotated dataset of 3,848 messages that will be useful to understand the various dimensions of WhatsApp political campaigning. We present several complementary insights into the investigative and sensational news stories from the same period. Exploratory data analysis and experiments showcase several exciting results and future research opportunities. To facilitate reproducible research, we make the anonymized datasets available in the public domain.

## introduction in the last decade, the majority of the political parties around the world are heavily spending on social media engagement platforms like facebook, twitter, quora, whatsapp, and sharechat for fast and secure information spread. whatsapp messenger (hereafter 'wam') is highly prevalent in 180 countries with installation over 90% of devices @xcite . wam allows users to send instant messages, photos, videos, and voice messages in addition to voice and video calls over a secure end-to-end encryption channel. however, data curation from wam remains a challenging task owing to privacy concerns, stringent encryption strategies, and system requirements. wam as political propaganda tool: several investigative journalism stories @xcite @xcite suggest ever-increasing global penetration of wam-based political propaganda and the resultant mass polarization effects. india, the second-most-populous country with 1.3 billion population, has witnessed similar trends @xcite during the past two general elections @xcite . wam has emerged as a primary leader for delivery for political messaging with 95% of android devices in india having wam installation and maintaining a 75% daily active users percentage @xcite . for the first time, complementing the existing investigative journalism stories, we present a scientific study to understand the wam messaging patterns and spread in the political scenario. wam data curation: to adhere to the wam's privacy policy @xcite , we consider only wam public groups where users willingly share messages with known and unknown people. the biggest challenge with restricting to public groups is a large number of fake and dubious groups and the unavailability of a standard in-app filter functionality to identify such groups. to the best of our information, none of the previous works on wam data curation have discussed any methodologies for filtering irrelevant groups. one of the earliest works on wam dataset curation @xcite considers a list of all wam public groups available online. we claim that due to multilingualism coupled with code-mixing and multi-modal metadata, the genuine group identification problem is non-trivial. we present a novel manual group filtering strategy to filter fake and dubious groups by leveraging group metadata, i.e., group name, display picture, and the description. owing to the user's privacy in the wam groups, we release the anonymized dataset foot_0 . in addition, we are also releasing a fine-grained annotated dataset of 3,848 messages (see section 3.2) for future research opportunities. the annotated dataset contains interesting fine-grained labels in four categories i.e., malicious activity, political orientation, political inclination, and message language. our contributions: the main contributions are: • we present a novel wam group filtering strategy leveraging the group's metadata to filter the fake and dubious groups. ## related work people engage in various social media platforms such as twitter, facebook, etc., to discuss socially relevant topics such as politics. we witness a large volume of research focused on twitter-based political discussions due to the easier availability of data and the large-scale involvement of the masses. for example, @xcite conducted a large-scale analysis of fake news propagation on twitter during the 2016 u.s. presidential elections. @xcite studied the usage of twitter as a tool for political deliberation and election forecasting. they also used twitter as a means to understand the political sentiment of the politicians and parties in the election campaigning. @xcite presented a study of the interaction between the network of users of different ideologies. they used twitter as a medium to understand the political communication network. @xcite studied the structure of political homophily between different political groups on twitter to understand the public sphere and echo chamber effect. in contrast to twitter-based studies, @xcite ) studied student's involvement in the political discussions on facebook for the 2008 u.s. presidential election. several studies looked at different offline modes of political discussions as well. for example, @xcite explored the effect of newspaper endorsement on the shift of vote margin. the largest circulation newspaper during the period of 1940-2002 is part of this experiment for the various u.s. elections. @xcite conducted a study to understand the visual perception of the voter by the candidate's photograph in the newspaper. @xcite proposed an innovative methodology on non-representative polls to forecast elections in contrast to the survey methods. they experiment with the data from the xbox gaming platform for the 2012 u.s. presidential election. @xcite discuss the involvement of the ideological communities over 15 years. they used data on the public voting of brazil and the u.s and study the polarization in the communities. @xcite presented an analysis and characterization of wam messages at three different layers. recently, we observe a growing interest in understanding the information flow on wam with political discussions in focus @xcite @xcite @xcite @xcite . in contrast to the existing works, we present a novel manual filtering strategy (see section 3) to identify the potential wam groups. our group identification strategy results in a high-quality dataset with less dubious groups. also, to understand the various user and message characteristics, we present a fine-grained annotated dataset of 3,848 messages. ## dataset in contrast to other social media platforms, data curation from wam public groups is a non-trivial task. even, group identification itself is a challenging task as wam does not support advanced content search feature. majority of the online fo-rums/blogs 23 comprises dubious public group links containing pornographic content, lucrative job, and lottery offers (see figure 1 ). as the initial step of data curation, we identify and join the relevant groups. we construct a seed set of indian political wam groups (based on the names of political parties and their top leaders) from several forums/blogs and social media platforms like google, facebook, and twitter. the seed set consists of 50 groups. the seed set is obtained after manual pre-processing of ∼600 groups. further, we enrich the seed set by following groups that were shared within the followed groups. overall, we obtained 2600 group links collected between january 19, 2019 -may 19, 2019. out of 2600, we only identify 281 (∼11%) relevant groups that are actively participating in indian political discussions. groups in which all the three metadata categories (i.e., name, display picture, and description) are indicative of the same political affiliation are considered genuine and relevant. for decryption of the wam database stored locally on the mobile device, we use a cipher key and a database extractor tool foot_3 . we divide the complete data collection into three phases: • before elections: january 19, 2019 -march 5, 2019 • active campaigning phase: march 6, 2019 -may 19, 2019 • after elections: may 20, 2019 -june 15, 2019 figure 2 presents user and content statistics at three different phases. we observe a sharp decline in the message count after elections. similarly, before elections, we find a low user count. thus, in the rest of the paper, we focus on the active campaigning phase, where the maximum number of users has shared the highest number of messages. ## a study on political and social co-occurrences the majority of the political parties often declare the agendas for their election campaigns. these agendas are mostly driven by various factors that touch upon the needs, beliefs, and necessities of the stakeholders. during the election campaign, the information propagation on various social media platforms gets heavily influenced by these social factors. in this section, we present a discussion on the cooccurrence of two political entities (i.e., leaders and parties) and four social factors (i.e., agenda, religion, caste, and languages/ethnicity) in the dataset. to this end, we learn the word representation using fasttext @xcite on the textual data in the messages. we pre-filter the noisy content in the dataset i.e. emoticons, special characters, and hyperlinks. to learn the representation, we use gensim foot_6 with python interfaces and default parameters. english and hindi are the two most popular languages in the dataset. the word representations from fasttext has different representation for the same word in two languages. to address this challenge, we translate the political entities and the social factors in the english languages (roman script) to the hindi language (devanagari script) using google translate foot_7 . to understand the co-occurrence of the political entities and social factors in the dataset, we compute the cosine similarity between them. for similarity computation, we use the words in both languages (i.e. english and hindi). the similarity between political entity w 1 and social factor w 2 is given as: sim(w 1 , w 2 ) = max{sim(hw 1 , hw 2 ), sim(hw 1 , ew 2 ), sim(ew 1 , hw 2 ), sim(ew 1 , ew 2 )}, where hw i and ew i represents the word representation of word w i in the hindi and the english language respectively. for the entity/social factor with more than one word, we take the average of the word representation of the constituent words. ## conclusion and future work in this paper, we present an exploration of the noisy user-generated data on wam with a focus on indian general elections 2019. in contrast to the existing works, we discuss a novel manual group filtering strategy to reduce the presence of noisy and dubious groups in the dataset. in addition, we present a fine-grained annotated dataset with multidimensional labels to understand various linguistic characteristics. lastly, the co-occurrence analysis put forward several interesting insights from the indian general elections 2019. in the future, the presented dataset would be useful in understanding various aspects (e.g., named-entities, pos tagging, language identification, etc.) of the noisy user generated textual data.