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"Yasseri, Taha"
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Controversy around climate change reports: a case study of Twitter responses to the 2019 IPCC report on land
In August 2019, the Intergovernmental Panel on Climate Change (IPCC) published its Special Report on Climate Change and Land (SRCCL), which generated extensive societal debate and interest in mainstream and social media. Using computational and conceptual text analysis, we examined more than 6,000 English-language posts on Twitter to establish the relative presence of different topics. Then, we assessed their levels of toxicity and sentiment polarity as an indication of contention and controversy. We find first that meat consumption and dietary options became one of the most discussed issues on Twitter in response to the IPCC report, even though it was a relatively minor element of the report; second, this new issue of controversy (meat and diet) had similar, high levels of toxicity to strongly contentious issues in previous IPCC reports (skepticism about climate science and the credibility of the IPCC). We suggest that this is in part a reflection of increasingly polarized narratives about meat and diet found in other areas of public discussion and of a movement away from criticism of climate science towards criticism of climate solutions. Finally, we discuss the possible implications of these findings for the work of the IPCC in anticipating responses to its reports and responding to them effectively.
Journal Article
Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data
Use of socially generated \"big data\" to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between \"real time monitoring\" and \"early predicting\" remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.
Journal Article
Even good bots fight: The case of Wikipedia
In recent years, there has been a huge increase in the number of bots online, varying from Web crawlers for search engines, to chatbots for online customer service, spambots on social media, and content-editing bots in online collaboration communities. The online world has turned into an ecosystem of bots. However, our knowledge of how these automated agents are interacting with each other is rather poor. Bots are predictable automatons that do not have the capacity for emotions, meaning-making, creativity, and sociality and it is hence natural to expect interactions between bots to be relatively predictable and uneventful. In this article, we analyze the interactions between bots that edit articles on Wikipedia. We track the extent to which bots undid each other's edits over the period 2001-2010, model how pairs of bots interact over time, and identify different types of interaction trajectories. We find that, although Wikipedia bots are intended to support the encyclopedia, they often undo each other's edits and these sterile \"fights\" may sometimes continue for years. Unlike humans on Wikipedia, bots' interactions tend to occur over longer periods of time and to be more reciprocated. Yet, just like humans, bots in different cultural environments may behave differently. Our research suggests that even relatively \"dumb\" bots may give rise to complex interactions, and this carries important implications for Artificial Intelligence research. Understanding what affects bot-bot interactions is crucial for managing social media well, providing adequate cyber-security, and designing well functioning autonomous vehicles.
Journal Article
Individual differences in knowledge network navigation
With the rapid accumulation of online information, efficient web navigation has grown vital yet challenging. To create an easily navigable cyberspace catering to diverse demographics, understanding how people navigate differently is paramount. While previous research has unveiled individual differences in spatial navigation, such differences in knowledge space navigation remain sparse. To bridge this gap, we conducted an online experiment where participants played a navigation game on Wikipedia and completed personal information questionnaires. Our analysis shows that age negatively affects knowledge space navigation performance, while multilingualism enhances it. Under time pressure, participants’ performance improves across trials and males outperform females, an effect not observed in games without time pressure. In our experiment, successful route-finding is usually not related to abilities of innovative exploration of routes. Our results underline the importance of age, multilingualism and time constraint in the knowledge space navigation.
Journal Article
Dynamics of Conflicts in Wikipedia
In this work we study the dynamical features of editorial wars in Wikipedia (WP). Based on our previously established algorithm, we build up samples of controversial and peaceful articles and analyze the temporal characteristics of the activity in these samples. On short time scales, we show that there is a clear correspondence between conflict and burstiness of activity patterns, and that memory effects play an important role in controversies. On long time scales, we identify three distinct developmental patterns for the overall behavior of the articles. We are able to distinguish cases eventually leading to consensus from those cases where a compromise is far from achievable. Finally, we analyze discussion networks and conclude that edit wars are mainly fought by few editors only.
Journal Article
Circadian Patterns of Wikipedia Editorial Activity: A Demographic Analysis
Wikipedia (WP) as a collaborative, dynamical system of humans is an appropriate subject of social studies. Each single action of the members of this society, i.e., editors, is well recorded and accessible. Using the cumulative data of 34 Wikipedias in different languages, we try to characterize and find the universalities and differences in temporal activity patterns of editors. Based on this data, we estimate the geographical distribution of editors for each WP in the globe. Furthermore we also clarify the differences among different groups of WPs, which originate in the variance of cultural and social features of the communities of editors.
Journal Article
What, when and where of petitions submitted to the UK government during a time of chaos
In times marked by political turbulence and uncertainty, as well as increasing divisiveness and hyperpartisanship, Governments need to use every tool at their disposal to understand and respond to the concerns of their citizens. We study issues raised by the UK public to the Government during 2015–2017 (surrounding the UK EU membership referendum), mining public opinion from a data set of 10,950 petitions, which contain 30.5 million signatures. We extract the main issues with a ground-up natural language processing method, latent Dirichlet allocation topic modelling. We then investigate their temporal dynamics and geographic features. We show that whilst the popularity of some issues is stable across the 2 years, others are highly influenced by external events, such as the referendum in June 2016. We also study the relationship between petitions’ issues and where their signatories are geographically located. We show that some issues receive support from across the whole country, but others are far more local. We then identify six distinct clusters of constituencies based on the issues which constituents sign. Finally, we validate our approach by comparing the petitions’ issues with the top issues reported in Ipsos MORI survey data. These results show the huge power of computationally analysing petitions to understand not only
what
issues citizens are concerned about but also
when
and from
where
.
Journal Article
A Practical Approach to Language Complexity: A Wikipedia Case Study
In this paper we present statistical analysis of English texts from Wikipedia. We try to address the issue of language complexity empirically by comparing the simple English Wikipedia (Simple) to comparable samples of the main English Wikipedia (Main). Simple is supposed to use a more simplified language with a limited vocabulary, and editors are explicitly requested to follow this guideline, yet in practice the vocabulary richness of both samples are at the same level. Detailed analysis of longer units (n-grams of words and part of speech tags) shows that the language of Simple is less complex than that of Main primarily due to the use of shorter sentences, as opposed to drastically simplified syntax or vocabulary. Comparing the two language varieties by the Gunning readability index supports this conclusion. We also report on the topical dependence of language complexity, that is, that the language is more advanced in conceptual articles compared to person-based (biographical) and object-based articles. Finally, we investigate the relation between conflict and language complexity by analyzing the content of the talk pages associated to controversial and peacefully developing articles, concluding that controversy has the effect of reducing language complexity.
Journal Article
Wikipedia traffic data and electoral prediction: towards theoretically informed models
This aim of this article is to explore the potential use of Wikipedia page view data for predicting electoral results. Responding to previous critiques of work using socially generated data to predict elections, which have argued that these predictions take place without any understanding of the mechanism which enables them, we first develop a theoretical model which highlights why people might seek information online at election time, and how this activity might relate to overall electoral outcomes, focussing especially on information seeking incentives related to swing voters and new parties. We test this model on a novel dataset drawn from a variety of countries in the 2009 and 2014 European Parliament elections. We show that while Wikipedia offers little insight into absolute vote outcomes, it does offer good information about changes in overall turnout at elections and about changes in vote share for particular parties. These results are used to enhance existing theories about the drivers of aggregate patterns in online information seeking, by suggesting that voters are cognitive misers who seek information only when considering changing their vote.
Journal Article