Linguistic patterns in social media content from crisis and non-crisis zones: A case study of Hurricane Ian

Abstract

Social media platforms, particularly Twitter, play a vital role in crisis response by delivering real-time information about affected populations. To enhance the accurate detection of crisis-relevant content, this study investigates linguistic distinctions between crisis and non-crisis zones. By analyzing over 263,000 tweets from within and outside the 2022 Hurricane Ian’s impact zone, we examine normalized word frequency, syntactic categories (nouns, verbs, adjectives, adverbs), sentiment, and user interaction patterns in the tweet networks. Our findings reveal a consistent power-law distribution in the relative differences of word use between crisis and non-crisis zones. Syntactic categories differences, particularly in adjectives, highlight the crisis zone’s emphasis on the hurricane’s path and impact, while the non-crisis zone’s vocabulary centers on current news topics such as sports, politics, and leisure. Syntactic analyses show that 36% (N = 20,967) of words are used in both crisis and non-crisis zones, 29% (N = 17,168) are unique to the crisis zone, and another 35% (N = 20,101) are unique to the non-crisis zone, highlighting the broader range of topics discussed in the non-crisis zone compared to the crisis zone. Sentiment analysis indicates comparable distributions of neutral words ($\sim$ 99%), followed by negative words ($\sim$ 0.4%) and positive words ($\sim$ 0.4%). However, the use of profanity, indicating strong negative sentiment, occurred 19% more frequently in non-crisis zone tweets than in crisis zone tweets. Network analysis and network modeling show that the crisis zone network is denser and more cohesive, reflecting tight-knit communities during crises, whereas the non-crisis zone network is larger and more fragmented, indicating diverse user engagements. Our study’s contributions include providing insights into the distinctive usage of words in crisis and non-crisis zones, hence facilitating the evaluation of crisis-relevant language patterns. Ultimately, the findings may be used to aid responders in prioritizing urgent tweets originating from a crisis zone.