Social Media Attributions in the Context of Water Crisis (2020)

Abstract: Attribution of natural disasters/collective misfortune is a widely-studied political science problem. However, such studies typically rely on surveys, or expert opinions, or external signals such as voting outcomes. In this paper, we explore the viability of using unstructured, noisy social media data to complement traditional surveys through automatically extracting attribution factors. We present a novel prediction task of attribution tie detection of identifying the factors (e.g., poor city planning, exploding population etc.) held responsible for the crisis in a social media document. We focus on the 2019 Chennai water crisis that rapidly escalated into a discussion topic with global importance following alarming water-crisis statistics. On a challenging data set constructed from YouTube comments (72,098 comments posted by 43,859 users on 623 videos relevant to the crisis), we present a neural baseline to identify attribution ties that achieves a reasonable performance (accuracy: 87.34% on attribution detection and 81.37% on attribution resolution). We release the first annotated data set of 2,500 comments in this important domain.

## introduction water crisis is one of the pressing current environmental challenges. more than a billion people do not have access to clean drinking water, and every year nearly two million children die from water borne diseases @xcite . one-third of the world's most extensive groundwater systems are under severe stress @xcite . the forecasts look even more grim; nearly two-thirds of the world population could be water stressed by 2025 @xcite . while the crisis has reached an alarming level far and wide, india is listed as one of the major at-risk countries @xcite . in june 2019, the longstanding chennai water crisis (washingtonpost, 2019) escalated into an international talking point, revealing alarming statistics of the water crisis in india looming in near future. in this context, we define a new task of inferring attribution ties through large scale analysis of relevant social media discussions. our main contributions in this paper are the following. social: apportioning attribution for a collective crisis or misfortune still remains a challenge in social science, despite the presence of a large body of political science literature on retrospective voting (see, e.g., @xcite or psychological literature on attribution @xcite . prior social science literature @xcite primarily relies on traditional surveys for attribution analysis. unlike traditional surveys, social media analyses are vastly cheaper, have faster turnaround time, can be conducted at different spatiotemporal granularities and aggregate a larger number of opinions than traditional surveys can usually afford. for instance, the most-recent pew survey (pew) focused on india was conducted in 2018 on only 2,521 users. in contrast, our data set consists of comments from 43,859 users. data set on crisis attribution: to the best of our knowledge, we present the first large scale social media analysis of the chennai water crisis via a substantial corpus of 72,098 youtube comments posted by 43,859 users on 623 relevant videos. our choice of youtube is informed by (1) its global reach; (2) its popularity in the indian subcontinent @xcite ; and ## related work water crisis has received sustained research focus in a diverse set of fields such as food policy research (hanjra and qureshi, 2010), earth science @xcite , social science @xcite , and water research @xcite @xcite , encompassing a broad range of dimensions including the socio-hydrological, ethical, cultural, and foreign policy aspects of the crisis. our work relies on these lines of research to compile a list of possible attribution factors (see, methodologically, our work is closely related with automatic extraction of blame ties @xcite . similar to @xcite , we seek to extract causal ties @xcite between a crisis and different possible factors. however, unlike the present work, @xcite focused on a clean corpus obtained from three major us newspapers. in contrast, we embrace the challenge of detecting attribution ties from noisy, social me-dia data which involves the following challenges. first, these discussions are produced in a part of the globe where vast majority of content contributors are non-native speakers of english. second, social media discussions encompass a diverse set of expressions ranging from stating pure fact or statistics to crude disgust and subtle sarcasm, and attribution topics are often expressed in widely different ways. for example, both the comments, 'we must protect our forests plant more trees' and 'just rewind and see how many trees have vanished over the years to accommodate more space for buildings and malls' deemed deforestation responsible for water crisis but have different ways of expressing it. in contrast, @xcite dealt with a set of well constructed entities that are easy to detect in a sentence due to their crisp word boundaries. ## results we summarize the performance of our baseline and models in since many of the attribution factors are semantically close (e.g., loss of water bodies, water withdrawal), we also consider a relaxed resolution criterion where a resolution is evaluated as correct if the models' top three predictions have an overlap with the ground truth as described in section 5.2. climate change | no attribution there is no proof of climate change droughts and floods are all natural phenomenon they have happened before there were humans also public water wastage | human activity the best way to save water is to stop consuming animal products so much of our precious water is used for animal agriculture government inaction | urbanization urban people are the reason for water shortage overpopulation | no attribution it has nothing to do with population control human activity | government inaction otherwise all our development is a waste if the people are being eliminated by carcinogens created due our irresponsible administration