Identifying Human Needs through Social Media: A study on I ndian cities during COVID -19 (2022)

Abstract: In this paper, we present a minimally-supervised approach to identify human needs expressed in tweets. Taking inspiration from Frustration-Aggression theory, we trained RoBERTa model to classify tweets expressing frustration which serves as an indicator of unmet needs. Although the notion of frustration is highly subjective and complex, the findings support the use of pretrained language model in identifying tweets with unmet needs. Our study reveals the major causes behind feeling frustrated during the lockdown and the second wave of the COVID-19 pandemic in India. Our proposed approach can be useful in timely identification and prioritization of emerging human needs in the event of a crisis.

## introduction india reported its first case of covid-19 from kerala in the month of jan, 2020 @xcite . several control measures including restrictions on international travel, screening of flight passengers, and institutional quarantine were undertaken shortly after to combat the transmission. the government of india (goi) imposed a nationwide lockdown foot_0 on mar 25, 2020 as a preventive measure to curb the spread of covid-19. lockdown is an emergency protocol that restricts nonessential movement of people as well as goods. this lockdown was eventually extended till may 31, 2020, making it one of the longest lockdowns imposed during the pandemic. this resulted in a huge gap in demand and supply of goods @xcite , increased stress @xcite and mass exodus of migrant workers from cities due to lack of earning opportunities in the informal economy @xcite . amidst the growing panic, twitter emerged as the go to platform to express one's feelings and needs such as travel, food, hospital beds, oxygen, cremation and funds foot_2 . an overwhelming number of tweets seeking support, lack of timely response and inadequate after-care are a few motivating factors behind this study. we particularly study the tweets from metropolitan indian cities posted during the covid-19 pandemic. the main contributions are as follows: the rest of the paper is organized as follows. section 2 describes the prior work pertinent to the research presented here. we present our approach to gather the needs from twitter in section 3. we introduce a roberta based classifier to detect unmet needs in section 4. we discuss the social impact of the proposed work in section 5 and list down the limitations in section 6. we conclude our work in section 7. ## background understanding human needs is a widely researched domain by state agencies as well as commercial organizations @xcite . prior research has shown that fulfilled needs have a positive impact on a person's feelings of well-being @xcite . from stockpiling basic household items during the initial phase of the pandemic to embracing digital technologies such as zoom, the market witnessed quite a shift in consumer needs since the outbreak of covid-19 @xcite . identifying one's true needs, however, is a challenging task. @xcite took inspiration from maslow's theory of motivation to predict consumer's needs and purchasing behavior using social media. ko et al. (2020) used korean twitter and blogs to discover customer's unmet needs through hierarchical concept search space algorithm. their approach aimed to facilitate idea generation for home appliances. more recently, yang et al. (2021) advocated the use of weibo foot_4 to identify unmet non-covid-19 healthcare needs. suh et al. (2021) studied the transition in needs during covid-19 through the search queries on bing. the product type in search queries were manually marked with the needs as described in maslow's theory of motivation to automate the task of need identification. their results affirmed a human tendency to first satisfy basic needs such as food and shelter before exploring advanced needs such as creativity and love. jolly et al. (2020) performed a psychometric analysis of tweets posted in response to official bulletins on covid-19 by state agencies, revealing the causality between bulletins and the feeling of medical emergency on twitter. prior studies @xcite @xcite have consistently demonstrated the efficacy of social media platforms such as twitter in capturing the feelings of society at scale. however, this unfurls the challenge of annotating the posts with their expressed needs. in ## identifying human needs from tweets we illustrate the different components of the proposed model in fig. 1 . the block diagram represents the steps of the proposed approach that are, (a) extracting key topics from twitter discourse, (b) manual mapping of the topics to the expressed human needs, (c) mapping tweets to needs assigned to their dominant topics and (d) detection of unmet needs from tweets. the components in green represent the use case of detecting unmet needs and categorization when given a live stream of tweets. ## detecting unmet needs unmet needs are widely characterized by frustration @xcite . through frustration-aggression theory, @xcite defined frustration as an impediment or blockage in achieving one's needs or goals. an impediment to a goal is considered frustration if and only if the person is actively striving to reach this goal. we thus hypothesize that an unmet need can be detected by identifying whether a tweet with expressed need has frustration or not. ## limitations due to our focus on understanding the pattern of needs emerged in india during the covid-19 pandemic, we performed rigorous filtering to retain only those tweets geo-tagged with locations within india. this significantly reduced the quantity of tweets gathered for our study. human needs are innately complex and ever evolving concept. as we transition from basic to advanced needs, the needs become more obscure and implicit. to optimize the time and effort for human annotation, we assumed that the dominant topic of a tweet would reflect its need type as discussed in section 3. this had an impact on the quality of tweet-need mapping and resulted in incorrect labeling in some cases. ## conclusion in this paper, we examined the human needs expressed in indian cities during the covid-19 pandemic. we described a minimally supervised approach to annotate tweets with their need level as in maslow's hierarchy of needs. this greatly reduced the time and human effort without much impact on the quality of annotation. we observed a recurring pattern in the needs, indicating predictability in the emerging needs in the event of a crisis. the results support the use of pretrained language model for the task of unmet needs detection. in future, we will extend the proposed model to detect needs in regional languages. we will further work upon incorporating theories better suited to capture advanced psychological needs.