Real-Time Classification of Twitter Trends

Social media users give rise to social trends as they share about common interests, which can be triggered by different reasons. In this work, we explore the types of triggers that spark trends on Twitter, introducing a typology with following four types: 'news', 'ongoing events', 'memes', and 'commemoratives'. While previous research has analyzed trending topics in a long term, we look at the earliest tweets that produce a trend, with the aim of categorizing trends early on. This would allow to provide a filtered subset of trends to end users. We analyze and experiment with a set of straightforward language-independent features based on the social spread of trends to categorize them into the introduced typology. Our method provides an efficient way to accurately categorize trending topics without need of external data, enabling news organizations to discover breaking news in real-time, or to quickly identify viral memes that might enrich marketing decisions, among others. The analysis of social features also reveals patterns associated with each type of trend, such as tweets about ongoing events being shorter as many were likely sent from mobile devices, or memes having more retweets originating from a few trend-setters.Comment: Pre-print of article accepted for publication in Journal of the American Society for Information Science and Technology copyright @ 2013 (American Society for Information Science and Technology

The Business and Culture of Social Media: In Search of the People Formerly Known As the Audience

This presentation addresses three transformations: the transformation of the audience; of advertising models; and of media businesses. The talk describes how they were transformed first by digital technology, and how they are now being transformed by social media. It goes on to describe what we call the "three economies" which govern the era of social media and proposes some research needed in order to understand and to monetize the audiences of this era

Tracking Dengue Epidemics using Twitter Content Classification and Topic Modelling

Detecting and preventing outbreaks of mosquito-borne diseases such as Dengue and Zika in Brasil and other tropical regions has long been a priority for governments in affected areas. Streaming social media content, such as Twitter, is increasingly being used for health vigilance applications such as flu detection. However, previous work has not addressed the complexity of drastic seasonal changes on Twitter content across multiple epidemic outbreaks. In order to address this gap, this paper contrasts two complementary approaches to detecting Twitter content that is relevant for Dengue outbreak detection, namely supervised classification and unsupervised clustering using topic modelling. Each approach has benefits and shortcomings. Our classifier achieves a prediction accuracy of about 80\% based on a small training set of about 1,000 instances, but the need for manual annotation makes it hard to track seasonal changes in the nature of the epidemics, such as the emergence of new types of virus in certain geographical locations. In contrast, LDA-based topic modelling scales well, generating cohesive and well-separated clusters from larger samples. While clusters can be easily re-generated following changes in epidemics, however, this approach makes it hard to clearly segregate relevant tweets into well-defined clusters.Comment: Procs. SoWeMine - co-located with ICWE 2016. 2016, Lugano, Switzerlan

Prédiction de la détérioration du comportement à l’aide de l’apprentissage automatique

Les plateformes de médias sociaux rassemblent des individus pour interagir de manière amicale et civilisée tout en ayant des convictions et des croyances diversifiées. Certaines personnes adoptent des comportements répréhensibles qui nuisent à la sérénité et affectent négativement l’équanimité des autres utilisateurs. Certains cas de mauvaise conduite peuvent initialement avoir de petits effets statistiques, mais leur accumulation persistante pourrait entraîner des conséquences majeures et dévastatrices. L’accumulation persistante des mauvais comportements peut être un prédicteur valide des facteurs de risque de détérioration du comportement. Le problème de la détérioration du comportement n’a pas été largement étudié dans le contexte des médias sociaux. La détection précoce de la détérioration du comportement peut être d’une importance cruciale pour éviter que le mauvais comportement des individus ne s’aggrave. Cette thèse aborde le problème de la détérioration du comportement dans le contexte des médias sociaux. Nous proposons de nouvelles méthodes basées sur l’apprentissage automatique qui (1) explorent les séquences comportementales et leurs motifs temporels pour faciliter la compréhension des comportements manifestés par les individus et (2) prédisent la détérioration du comportement à partir de combinaisons consécutives de motifs séquentiels correspondant à des comportements inappropriés. Nous menons des expériences approfondies à l’aide d’ensembles de données du monde réel et démontrons la capacité de nos modèles à prédire la détérioration du comportement avec un haut degré de précision, c’est-à-dire des scores F-1 supérieurs à 0,8. En outre, nous examinons la trajectoire de détérioration du comportement afin de découvrir les états émotionnels que les individus présentent progressivement et d’évaluer si ces états émotionnels conduisent à la détérioration du comportement au fil du temps. Nos résultats suggèrent que la colère pourrait être un état émotionnel potentiel qui pourrait contribuer substantiellement à la détérioration du comportement

Sovereign wealth funds : Past, present and future

Author's accepted manuscript.Available from 16/11/2021.In this article, we conduct a meta-literature review of sovereign wealth funds (SWFs), covering 184 articles from 2005 to 2019. Our meta-literature review consists of qualitative analysis of content using the NVivo software program and quantitative analyses of bibliometric citations using the HistCite and VOSviewer software programs. We identify three main research streams: (i) the overview and growth of SWFs, (ii) governance and political concerns regarding SWFs, and (iii) the investment strategies of SWFs. We identify the most influential aspects of the SWF literature, such as the leading countries, institutions, journals, authors, and articles. Finally, we propose 20 research questions based on the meta-literature review of sovereign wealth funds to set the future research agenda.acceptedVersio

Channeling Change: Making Collective Impact Work

Large-scale social change requires broad cross-sector coordination, yet the social sector remains focused on the isolated intervention of individual organizations. Substantially greater progress could be made in alleviating many of our most serious and complex social problems if nonprofits, governments, businesses, and the public were brought together around a common agenda to create collective impact. Published in the Stanford Social Innovation Review, Winter 2011

The Platform Press: How Silicon Valley Reengineered Journalism

The influence of social media platforms and technology companies is having a greater effect on American journalism than even the shift from print to digital. There is a rapid takeover of traditional publishers’ roles by companies including Facebook, Snapchat, Google, and Twitter that shows no sign of slowing, and which raises serious questions over how the costs of journalism will be supported. These companies have evolved beyond their role as distribution channels, and now control what audiences see and who gets paid for their attention, and even what format and type of journalism flourishes. Publishers are continuing to push more of their journalism to third-party platforms despite no guarantee of consistent return on investment. Publishing is no longer the core activity of certain journalism organizations. This trend will continue as news companies give up more of the traditional functions of publishers. This report, part of an ongoing study by the Tow Center for Digital Journalism at Columbia Journalism School, charts the convergence between journalism and platform companies. In the span of 20 years, journalism has experienced three significant changes in business and distribution models: the switch from analog to digital, the rise of the social web, and now the dominance of mobile. This last phase has seen large technology companies dominate the markets for attention and advertising and has forced news organizations to rethink their processes and structures

Platforms and the Fall of the Fourth Estate: Looking Beyond the First Amendment to Protect Watchdog Journalism

Journalists see the First Amendment as an amulet, and with good reason. It has long protected the Fourth Estate—an independent institutional press—in its exercise of editorial discretion to check government power. This protection helped the Fourth Estate flourish in the second half of the twentieth century and ably perform its constitutional watchdog role. But in the last two decades, the media ecology has changed. The Fourth Estate has been subsumed by a Networked Press in which journalists are joined by engineers, algorithms, audience, and other human and non-human actors in creating and distributing news. The Networked Press’s most powerful members are platforms. These platforms—companies like Facebook, Google, and Twitter—shun the media label even as they function as information gatekeepers and news editors. Their norms and values, including personalization and speed, stymie watchdog reporting. The Networked Press regime significantly threatens watchdog journalism, speech that is at the core of the press’s constitutional role. Yet, limited by the state action doctrine, the First Amendment cannot shield this speech from a threat by private actors like platforms. Today, the First Amendment is insufficient to protect a free press that can serve as a check on government tyranny. This article argues that we must look beyond the First Amendment to protect watchdog journalism from the corrosive power of platforms. It describes the limits of the First Amendment and precisely how platforms threaten watchdog journalism. It also proposes a menu of extra-constitutional options for bolstering this essential brand of speech