Predicting the active period of popularity evolution: A case study on Twitter hashtags

Huang, Jianyi; Tang, Yuyuan; Hu, Ying; Li, Jianjiang; Hu, Changjun

doi:10.1016/j.ins.2019.04.028

Cited by 16 publications

(9 citation statements)

References 12 publications

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“…This trend of the rich getting richer with respect to popularity in Google may reflect large recent social trends with respect to shared global icons (Huang et al. 2019). Meanwhile, because engagements with Wikipedia are usually deeper (O'Sullivan 2009), a more diverse general interest in nature may be maintained (Fig.…”

Section: Discussionmentioning

confidence: 99%

Combining culturomic sources to uncover trends in popularity and seasonal interest in plants

Vardi

Mittermeier

2021

Conservation Biology

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Culturomic tools enable the exploration of trends in human-nature interactions, although they entail inherent biases and necessitate careful validation. Furthermore, people may engage with nature across different culturomic data sets differently. We evaluated people's digital interest and engagement with plant species based on Wikipedia and Google data and explored the conservation implications of these temporal interest patterns. As a case study, we explored the digital footprints of the most popular plant species in Israel. We analyzed 4 years of daily page views from Hebrew Wikipedia and 10 years of daily Google search volume in Israel. We modeled popularity of plant species in these 2 data sets based on a suite of plant attributes. We further explored the seasonal trends of people's interest in each species. We found differences in how people interacted digitally with plants in Wikipedia and Google. Overall, in Google, searches for species that have utility to humans were more common, whereas in Wikipedia, plants that serve as cultural emblems received more attention. Furthermore, in Google, popular species attracted more attention over time, opposite to the trend in Wikipedia. In Google, interest in species with short bloom duration exhibited more pronounced seasonal patterns, whereas in Wikipedia, seasonality of interest increased as bloom duration increased. Together, our results suggest that people's digital interactions with nature may be inherently different depending on the sources explored, which may affect use of this information for conservation. Although culturomics holds much promise, better understanding of its underpinnings is important when translating insights into conservation actions.

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Section: Discussionmentioning

confidence: 99%

Combining culturomic sources to uncover trends in popularity and seasonal interest in plants

Vardi

Mittermeier

2021

Conservation Biology

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“…Finally, a common problem with network studies of this sort is relying on hashtags to filter the data space, as hashtags are complex [33] , and can be attempts to gain popularity [34] , rather than signaling genuine discussion. While we made no assumptions of motivation behind the formation of tweets on the part of the user, future work might consider parsing and exploring Twitter and relate online discussion data in a different way, such as tweets or discussions among specific communities like academia, finance, or government.…”

Section: Concluding Remarkmentioning

confidence: 99%

Uncovering the Online Social Structure Surrounding COVID-19

et al. 2021

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How do people talk about COVID-19 online? To address this question, we offer an unsupervised framework that allows us to examine Twitter framings of the pandemic. Our approach employs a network-based exploration of social media data to identify, categorize, and understand communication patterns about the novel coronavirus on Twitter. The simplest structure that emerges from our analysis is the distinction between the internal/personal, external/global, and generic threat framings of the pandemic. This structure replicates in different Twitter samples and is validated using the variation of information measure, reflecting the significance and stability of our findings. Such an exploratory study is useful for understanding the contours of the natural, non-random structure in this online space. We contend that this understanding of structure is necessary to address a host of causal, supervised, and related questions downstream.

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“…As hashtags são usadas em estudos realizados para diferentes fins, sejam aqueles dedicados à detecc ¸ão [Chen et al, 2018] e monitoramento de eventos [Giridhar et al, 2017], predic ¸ão de popularidade [Huang et al, 2020] ou sistemas de recomendac ¸ão [Alsini et al, 2020, Yang et al, 2020. Em contrapartida, Liu et al que desenvolvem um modelo de vetorizac ¸ão de hashtags para preservar a semântica, incorporam informac ¸ões extraídas do conteúdo textual e das múltiplas relac ¸ões estruturais dentro de um tweet [Liu et al, 2018].…”

Section: Trabalhos Relacionadosunclassified

“…As hashtags são frequentemente exploradas em diversas pesquisas, como detecc ¸ão [Chen et al, 2018] e monitoramento de eventos [Giridhar et al, 2017], predic ¸ão de popularidade [Huang et al, 2020], e sistemas de recomendac ¸ão [Alsini et al, 2020, Yang et al, 2020 incorporac ¸ão de semântica em vetores [Liu et al, 2018] ou modelagem de tópicos [Wang et al, 2016]. Outros trabalhos concentram-se na análise e caracterizac ¸ão de hashtags em torno de temas específicos [Cossard et al, 2020] ou empregam plataformas sociais não usuais [Yang et al, 2020].…”

Section: Introduc ¸ãOunclassified

Caracterização Sócio-Temporal de Conteúdos em Redes Sociais baseada em Processamento em Fluxo

Oliveira¹,

Medeiros²,

Mattos³

2021

Anais Do XXVI Workshop De Gerência E Operação De Redes E Serviços (WGRS 2021)

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A velocidade e a dinâmica de propagação de assuntos veiculados no Twitter caracterizam a plataforma como uma fonte de dados ininterrupta. Este artigo propõe uma abordagem distribuída baseada em métricas de redes complexas para a caracterização socio-temporal de dados textuais provenientes do Twitter. A proposta integra o Apache Kafka na ingestão dos dados e o Apache Spark Streaming no processamento em ﬂuxo dos dados para garantir a captura contínua e o processamento eficiente do conteúdo de diferentes fontes. A proposta identifica, correlaciona e monitora o uso de hashtags em tempo real, através de uma estrutura de grafo dinâmica, gerando uma ontologia sobre o tópico de interesse. Diferente de trabalhos anteriores, que empregam dados históricos, a proposta é aplicada a um caso de uso real com grande repercussão e engajamento dos usuários do Twitter. Avaliando as ﬂutuações de métricas como centralidade, diâmetro e densidade para múltiplas componentes do grafo de hashtags, os resultados revelam tendências de escrita e padrões de relacionamento que reforçam a sensação de câmaras de eco e oportunismo midiático na lógica de utilização de hashtags.

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Predicting the active period of popularity evolution: A case study on Twitter hashtags

Cited by 16 publications

References 12 publications

Combining culturomic sources to uncover trends in popularity and seasonal interest in plants

Combining culturomic sources to uncover trends in popularity and seasonal interest in plants

Uncovering the Online Social Structure Surrounding COVID-19

Caracterização Sócio-Temporal de Conteúdos em Redes Sociais baseada em Processamento em Fluxo

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