Wikimedia Research/Showcase/Archive/2022/02
February 2022
[edit]- Theme
- Collective Attention in Wikipedia
February 16, 2022 Video: YouTube
- Modeling Collective Anticipation and Response on Wikipedia
- By Renaud Lambiotte (University of Oxford)
- The dynamics of popularity in online media are driven by a combination of endogenous spreading mechanisms and response to exogenous shocks including news and events. However, little is known about the dependence of temporal patterns of popularity on event-related information, e.g. which types of events trigger long-lasting activity. Here we propose a simple model that describes the dynamics around peaks of popularity by incorporating key features, i.e., the anticipatory growth and the decay of collective attention together with circadian rhythms. The proposed model allows us to develop a new method for predicting the future page view activity and for clustering time series. To validate our methodology, we collect a corpus of page view data from Wikipedia associated to a range of planned events, that are events which we know in advance will have a fixed date in the future, such as elections and sport events. Our methodology is superior to existing models in both prediction and clustering tasks. Furthermore, restricting to Wikipedia pages associated to association football, we observe that the specific realization of the event, in our case which team wins a match or the type of the match, has a significant effect on the response dynamics after the event. Our work demonstrates the importance of appropriately modeling all phases of collective attention, as well as the connection between temporal patterns of attention and characteristic underlying information of the events they represent.
- Related paper: Modeling Collective Anticipation and Response on Wikipedia, ICWSM 2021 (pdf)
- Slides on figshare
- The dynamics of popularity in online media are driven by a combination of endogenous spreading mechanisms and response to exogenous shocks including news and events. However, little is known about the dependence of temporal patterns of popularity on event-related information, e.g. which types of events trigger long-lasting activity. Here we propose a simple model that describes the dynamics around peaks of popularity by incorporating key features, i.e., the anticipatory growth and the decay of collective attention together with circadian rhythms. The proposed model allows us to develop a new method for predicting the future page view activity and for clustering time series. To validate our methodology, we collect a corpus of page view data from Wikipedia associated to a range of planned events, that are events which we know in advance will have a fixed date in the future, such as elections and sport events. Our methodology is superior to existing models in both prediction and clustering tasks. Furthermore, restricting to Wikipedia pages associated to association football, we observe that the specific realization of the event, in our case which team wins a match or the type of the match, has a significant effect on the response dynamics after the event. Our work demonstrates the importance of appropriately modeling all phases of collective attention, as well as the connection between temporal patterns of attention and characteristic underlying information of the events they represent.
- By Renaud Lambiotte (University of Oxford)
- Sudden Attention Shifts on Wikipedia During the COVID-19 Crisis
- By Kristina GligoriÄ (EPFL)
- We study how the COVID-19 pandemic, alongside the severe mobility restrictions that ensued, has impacted information access on Wikipedia, the worldâs largest online encyclopedia. A longitudinal analysis that combines pageview statistics for 12 Wikipedia language editions with mobility reports published by Apple and Google reveals massive shifts in the volume and nature of information seeking patterns during the pandemic. Interestingly, while we observe a transient increase in Wikipediaâs pageview volume following mobility restrictions, the nature of information sought was impacted more permanently. These changes are most pronounced for language editions associated with countries where the most severe mobility restrictions were implemented. We also find that articles belonging to different topics behaved differently; e.g., attention towards entertainment-related topics is lingering and even increasing, while the interest in health- and biology-related topics was either small or transient. Our results highlight the utility of Wikipedia for studying how the pandemic is affecting peopleâs needs, interests, and concerns.
- Related paper: Sudden Attention Shifts on Wikipedia During the COVID-19 Crisis, ICWSM 2021 (pdf)
- Slides on figshare
- We study how the COVID-19 pandemic, alongside the severe mobility restrictions that ensued, has impacted information access on Wikipedia, the worldâs largest online encyclopedia. A longitudinal analysis that combines pageview statistics for 12 Wikipedia language editions with mobility reports published by Apple and Google reveals massive shifts in the volume and nature of information seeking patterns during the pandemic. Interestingly, while we observe a transient increase in Wikipediaâs pageview volume following mobility restrictions, the nature of information sought was impacted more permanently. These changes are most pronounced for language editions associated with countries where the most severe mobility restrictions were implemented. We also find that articles belonging to different topics behaved differently; e.g., attention towards entertainment-related topics is lingering and even increasing, while the interest in health- and biology-related topics was either small or transient. Our results highlight the utility of Wikipedia for studying how the pandemic is affecting peopleâs needs, interests, and concerns.
- By Kristina GligoriÄ (EPFL)