Reshaping the urban hierarchy: patterns of information diffusion on social media

Abstract

The spatial diffusion of information is a process governed by the flow of interpersonal communication. The emergence of the Internet and especially social media platforms has reshaped this process and previous research has studied how online social networks contribute to the diffusion of information. Understanding such processes can help devise methods to maximize or control the reach of information or even identify upcoming events and social movements. Yet activities in cyberspace are still confined to physical locations and this geographic connection tends to be overlooked. In this research, we focus on geographic regions instead of individuals and study how the underlying hierarchical structure of regions relates to their response to the information. We examined the top 30 populated cities and metropolitan areas in the U.S. and retrieved Twitter data related to two selected topics from these regions, the 2015 Nepal Earthquake and the #JesuisCharlie hashtag in response to the Paris attacks on the Charlie Hebdo offices. We analyzed the similarity among regions of their response using multiple statistical methods and three urban classifications. Our results indicate that the diffusion of information is impacted by the hierarchy of urban regions and that the Twitter responses act more similar when the populated regions are positioned at the same level in the urban hierarchy. ARTICLE HISTORY

Arabic Offensive and Hate Speech Detection Using a Cross-Corpora Multi-Task Learning Model

“…Natural language processing (NLP) is a field of research that deals with machine learning (ML) algorithms applied to human natural languages [5]. NLP applications aim to automatically process written and spoken human languages including sentiment analysis [6,7], sarcasm detection [8], machine translation [9], speech recognition [10], automated dialogue systems [11], urban studies [12,13], topic classification [14], similarity detection [15], text summarization [16], intent detection [17], news and social media analysis [18,19], part-of-speech (POS) tagging [20], authorship attribution [21,22], fake tweet detection [23], coreference resolution [24] and others [14,[25][26][27]. Recently, NLP techniques have also been employed to study the sentiments and attitudes of social media users regarding the COVID-19 pandemic [28,29].…”

Modelling impacts of high-speed rail on urban interaction with social media in China’s mainland

“…The urban interaction involves the movement of people and cargo between places. The urban interaction also can refer to virtual connections, such as information communication between places (Yang et al 2019;Ye and Wei 2019). Conventionally, survey data were used to measure urban interaction, such as volumes of passengers between two cities (Xiao et al 2013), migration flows (Flowerdew and Lovett 2010), trade flow (Hesse 2010), and telecommunications (Guldmann 2005).…”

“…Conventionally, survey data were used to measure urban interaction, such as volumes of passengers between two cities (Xiao et al 2013), migration flows (Flowerdew and Lovett 2010), trade flow (Hesse 2010), and telecommunications (Guldmann 2005). Compared with survey data, social media data offer a high temporal resolution, and are accessible at a large size with minimal cost (Cao et al 2015;Wang et al 2016;Li et al 2017;Yang et al 2019;Ghani et al 2019).…”

An investigation of the visual features of urban street vitality using a convolutional neural network

“…Urban public space is the key area for a resident to live its public urban life and is highly related to urban sustainable development (Kim and An 2017;Yang et al 2019). Vitality in urban public space refers to a safer, more desirable, and more attractive space that has the capacity to offer more choices for social activities and be a place for cultural exchanges.…”