What is Tumblr: A Statistical Overview and Comparison with other popular social services, including blogosphere,
Twitter and Facebook, in answering a couple of key
questions: What is Tumblr? How is Tumblr different from
other social media networks?
Twitter analytics: some thoughts on sampling, tools, data, ethics and user re...Farida Vis
Keynote delivered at the SRA Social Media in Social Research conference, London, 24 June, 2013. The presentation highlights some thoughts on sampling, tools, data, ethics and user requirements for Twitter analytics, including an overview of a series of recent tools.
SHORTer VERSION - Liminality and Communitas in Social Media - The case of Twi...Jana Herwig
A longer version, optimized for the lack of verbal input, can be found here: http://www.slideshare.net/anaj/liminality-and-communitas-in-social-media-the-case-of-twitter
The African Commons Project in collaboration with Sangonet regularly run a 1-day training workshop for South African NGOs, providing an introduction to social media tools and how they can be applied in their work for social good. This is an updated version of the course. More info at http://africancommons.org
The Influcence of Twitter on Academic EnvironmentMartin Ebner
Draft version of article of the book "Social Media and the New Academic Environment: Pedagogical Challenges" http://www.igi-global.com/book/social-media-new-academic-environment/69841#description
Twitter analytics: some thoughts on sampling, tools, data, ethics and user re...Farida Vis
Keynote delivered at the SRA Social Media in Social Research conference, London, 24 June, 2013. The presentation highlights some thoughts on sampling, tools, data, ethics and user requirements for Twitter analytics, including an overview of a series of recent tools.
SHORTer VERSION - Liminality and Communitas in Social Media - The case of Twi...Jana Herwig
A longer version, optimized for the lack of verbal input, can be found here: http://www.slideshare.net/anaj/liminality-and-communitas-in-social-media-the-case-of-twitter
The African Commons Project in collaboration with Sangonet regularly run a 1-day training workshop for South African NGOs, providing an introduction to social media tools and how they can be applied in their work for social good. This is an updated version of the course. More info at http://africancommons.org
The Influcence of Twitter on Academic EnvironmentMartin Ebner
Draft version of article of the book "Social Media and the New Academic Environment: Pedagogical Challenges" http://www.igi-global.com/book/social-media-new-academic-environment/69841#description
The African Commons Project in collaboration with Sangonet ran a 1-day training workshop for South African NGOs. The course provided an introduction to social media tools for NGOs
UNIT 1: INTRODUCTION
Introduction to Web - Limitations of current Web – Development of Semantic Web – Emergence of the Social Web – Statistical Properties of Social Networks -Network analysis - Development of Social Network Analysis - Key concepts and measures in network analysis - Discussion networks -Blogs and online communities - Web-based networks
Il laboratorio aperto: limiti e possibilità dell’uso di Facebook, Twitter e Y...Manolo Farci
Intervento di Davide Bennato, Fabio Giglietto, Luca Rossi tenuto durante il convegno "Così vicini, così lontani: la via italiana aia social network" (26-27 Settembre Milano)
Abstract:
This article examines how online groups are formed and sustained during crisis periods, especially when political polarization in society is at its highest level. We focus on the use of Vkontakte (VK), a popular social networking site in Ukraine, to understand how it was used by Pro- and Anti-Maidan groups during the 2013/2014 crisis in Ukraine. In particular, we ask whether and to what extent the ideology (or other factors) of a particular group shapes its network structure. We find some support that online social networks are likely to represent local and potentially preexisting social networks, likely due to the dominance of reciprocal (and often close) relationships on VK and opportunities for group members to meet face-to-face during offline protests. We also identify a number of group-level indicators, such as degree centralization, modularity index and average engagement level, that could help to classify groups based on their network properties. Community researchers can start applying these group-level indicators to online communities outside VK; they can also learn from this article how to identify networks of spam and marketing accounts.
Why should scientists care about social media and communications? Don Stanley of 3Rhino Media and the University of Wisconsin-Madison Department of Life Sciences Communication answers this question in this presentation.
He also addresses how to get started with LinkedIn as a first social media platform
Picturing the Social: Talk for Transforming Digital Methods Winter SchoolFarida Vis
This talk highlights the work of the Visual Social Media Lab and the Picturing the Social project. It summarises the key research questions and aims of the project. It highlights the value of interdisciplinarity and working closely with industry in this area. It also focuses on the way in which me might study different types of structures involved in the circulation and the scopic regimes that make social media images more or less visible. It also tries to unpack how we can start to think about APIs as 'method' and looks at the different ways in which we can get access to different kinds of social media image data. Both through public ('free') APIs and ('pay for') firehose data.
Presentation at the Workshop on "Small Data and Big Data Controversies and Alternatives: Perspectives from The Sage Handbook of Social Media Research Methods" with Anabel Quan-Haase, Luke Sloan, Diane Rasmussen Pennington, et al.
LINK: http://sched.co/7G5N
Much is written about using Twitter for research, but what about using it in learning and teaching? It has plenty of applications there as well.
This workshop presentation (containing a link to the handout) covers what Twitter is, why it's useful, debunks some Twitter myths, and illustrates ideas for Twitter use in modules, lectures and lab-sessions, using current examples from academics. It also covers embedding a Twitter stream in a Blackboard VLE.
Also covers the tools Twtpoll and Tweetbeam.
A guide for librarians for using Twitter as a means to teach information literacy. Presentation for Easy Bib Summer Professional Development Series. July 10, 2013.
Talk given at the Semantic Web SIKS course 2011: why we need semantics on the Social Web. Three examples: social tagging, user profiling based on Twitter streams and cross-system user profiling (linking user profiles).
Anatoliy Gruzd and Philip Mai
Workshop presented at the TTRA Annual International Conference in Quebec City (June 20, 2017)
https://2017ttraannualinternationalconfe.sched.com/event/9yCg/social-listening-how-to-do-it-and-how-to-use-it-veille-sociale-comment-faire-et-comment-lutiliser?iframe=no&w=100%&sidebar=no&bg=no
Communicating with Your Audience in 140 CharactersJenni Fuchs
"Museums in the Digital Age - Communicating with Your Audience in 140 Characters", presented at the ICOM-CECA Conference in Yerevan, Armenia, October 2012
Twitter is going to become much less important as a person to person (peer to peer) communication medium and instead become more of a content-delivery medium like TV, where content is broadcast to a large number of followers. It's just going to become a new way to follow celebrities, corporations, and the like.
These are the findings of research by two business school professors. Their paper "Intrinsic versus Image-Related Motivations in Social Media: Why do People Contribute to Twitter?", was published in the journal Marketing Science. The professors are Olivier Toubia of Columbia Business School and Andrew T. Stephen of the University of Pittsburgh.
In Professor Toubia’s words, “Get ready for a TV-like Twitter".
The research examined the motivations behind why everyday people, with no financial incentive, contribute to Twitter.The study examined roughly 2500 non-commercial Twitter users. In a field experiment, the profs randomly selected some of those users and, through the use of other synthetic accounts, increased the selected group's followers. At first, they noticed that as the selected group's followers increased, so did the posting rate. However, when that group reached a level of stature — a moderately large amount of followers — the posting rate declined significantly.
"Users began to realize it was harder to continue to attract more followers with their current strategy, so they slowed down.When posting activity no longer leads to additional followers, people will view Twitter as a non-evolving, static structure, like TV."
Based on the analyses, the profs predict Twitter posts by everyday people will slow down, yet celebrities and commercial users will continue to post for financial gain.
The paper is attached on Slideshare.
The African Commons Project in collaboration with Sangonet ran a 1-day training workshop for South African NGOs. The course provided an introduction to social media tools for NGOs
UNIT 1: INTRODUCTION
Introduction to Web - Limitations of current Web – Development of Semantic Web – Emergence of the Social Web – Statistical Properties of Social Networks -Network analysis - Development of Social Network Analysis - Key concepts and measures in network analysis - Discussion networks -Blogs and online communities - Web-based networks
Il laboratorio aperto: limiti e possibilità dell’uso di Facebook, Twitter e Y...Manolo Farci
Intervento di Davide Bennato, Fabio Giglietto, Luca Rossi tenuto durante il convegno "Così vicini, così lontani: la via italiana aia social network" (26-27 Settembre Milano)
Abstract:
This article examines how online groups are formed and sustained during crisis periods, especially when political polarization in society is at its highest level. We focus on the use of Vkontakte (VK), a popular social networking site in Ukraine, to understand how it was used by Pro- and Anti-Maidan groups during the 2013/2014 crisis in Ukraine. In particular, we ask whether and to what extent the ideology (or other factors) of a particular group shapes its network structure. We find some support that online social networks are likely to represent local and potentially preexisting social networks, likely due to the dominance of reciprocal (and often close) relationships on VK and opportunities for group members to meet face-to-face during offline protests. We also identify a number of group-level indicators, such as degree centralization, modularity index and average engagement level, that could help to classify groups based on their network properties. Community researchers can start applying these group-level indicators to online communities outside VK; they can also learn from this article how to identify networks of spam and marketing accounts.
Why should scientists care about social media and communications? Don Stanley of 3Rhino Media and the University of Wisconsin-Madison Department of Life Sciences Communication answers this question in this presentation.
He also addresses how to get started with LinkedIn as a first social media platform
Picturing the Social: Talk for Transforming Digital Methods Winter SchoolFarida Vis
This talk highlights the work of the Visual Social Media Lab and the Picturing the Social project. It summarises the key research questions and aims of the project. It highlights the value of interdisciplinarity and working closely with industry in this area. It also focuses on the way in which me might study different types of structures involved in the circulation and the scopic regimes that make social media images more or less visible. It also tries to unpack how we can start to think about APIs as 'method' and looks at the different ways in which we can get access to different kinds of social media image data. Both through public ('free') APIs and ('pay for') firehose data.
Presentation at the Workshop on "Small Data and Big Data Controversies and Alternatives: Perspectives from The Sage Handbook of Social Media Research Methods" with Anabel Quan-Haase, Luke Sloan, Diane Rasmussen Pennington, et al.
LINK: http://sched.co/7G5N
Much is written about using Twitter for research, but what about using it in learning and teaching? It has plenty of applications there as well.
This workshop presentation (containing a link to the handout) covers what Twitter is, why it's useful, debunks some Twitter myths, and illustrates ideas for Twitter use in modules, lectures and lab-sessions, using current examples from academics. It also covers embedding a Twitter stream in a Blackboard VLE.
Also covers the tools Twtpoll and Tweetbeam.
A guide for librarians for using Twitter as a means to teach information literacy. Presentation for Easy Bib Summer Professional Development Series. July 10, 2013.
Talk given at the Semantic Web SIKS course 2011: why we need semantics on the Social Web. Three examples: social tagging, user profiling based on Twitter streams and cross-system user profiling (linking user profiles).
Anatoliy Gruzd and Philip Mai
Workshop presented at the TTRA Annual International Conference in Quebec City (June 20, 2017)
https://2017ttraannualinternationalconfe.sched.com/event/9yCg/social-listening-how-to-do-it-and-how-to-use-it-veille-sociale-comment-faire-et-comment-lutiliser?iframe=no&w=100%&sidebar=no&bg=no
Communicating with Your Audience in 140 CharactersJenni Fuchs
"Museums in the Digital Age - Communicating with Your Audience in 140 Characters", presented at the ICOM-CECA Conference in Yerevan, Armenia, October 2012
Twitter is going to become much less important as a person to person (peer to peer) communication medium and instead become more of a content-delivery medium like TV, where content is broadcast to a large number of followers. It's just going to become a new way to follow celebrities, corporations, and the like.
These are the findings of research by two business school professors. Their paper "Intrinsic versus Image-Related Motivations in Social Media: Why do People Contribute to Twitter?", was published in the journal Marketing Science. The professors are Olivier Toubia of Columbia Business School and Andrew T. Stephen of the University of Pittsburgh.
In Professor Toubia’s words, “Get ready for a TV-like Twitter".
The research examined the motivations behind why everyday people, with no financial incentive, contribute to Twitter.The study examined roughly 2500 non-commercial Twitter users. In a field experiment, the profs randomly selected some of those users and, through the use of other synthetic accounts, increased the selected group's followers. At first, they noticed that as the selected group's followers increased, so did the posting rate. However, when that group reached a level of stature — a moderately large amount of followers — the posting rate declined significantly.
"Users began to realize it was harder to continue to attract more followers with their current strategy, so they slowed down.When posting activity no longer leads to additional followers, people will view Twitter as a non-evolving, static structure, like TV."
Based on the analyses, the profs predict Twitter posts by everyday people will slow down, yet celebrities and commercial users will continue to post for financial gain.
The paper is attached on Slideshare.
Who are the top influencers and what characterizes them?Nicola Procopio
A method capable of finding influential users by exploiting the contents of the messages posted by them to express opinions on items, by modeling these contents with a three-layer network.
this presentation shows .. ...which is the most effective social media platform today...and what are the enhancement of that social media to retain its position as no.1 in the next 3 years
In dem 2023 von der Europäischen Kommission angenommenen Vorschlag für eine Europäische Erklärung zum Radfahren wird das Radfahren als nachhaltiges, zugängliches und inklusives, erschwingliches und gesundes Verkehrsmittel mit hohem Mehrwert für die EU-Wirtschaft anerkannt. Darin werden Grundsätze zur Förderung des Radverkehrs aufgeführt, die als Richtschnur für künftige Maßnahmen in der EU dienen werden. Um die Qualität, Quantität, Kontinuität und Attraktivität der Radverkehrsinfrastruktur in allen Mitgliedstaaten zu verbessern, sind klare Verpflichtungen erforderlich, wie sichere und kohärente Radverkehrsnetze in Städten, bessere Verbindungen mit öffentlichen Verkehrsmitteln, sichere Parkplätze, die Einrichtung von Ladestationen für E-Bikes und Fahrradautobahnen, die Städte mit ländlichen Gebieten verbinden.
Mobilitätswende 2030: vom Linienbus zur öffentlichen Mobilität der ZukunftStephan Tschierschwitz
Verfasser: Fraunhofer IESE
Warum sich der straßengebundene öffentliche Personennahverkehr ändern
muss
Mobilität ist eng mit Lebensqualität verknüpft und ist zu Recht Teil der Daseinsvorsorge. Denn
Mobilität dient der Erfüllung grundlegender Bedürfnisse wie einkaufen gehen oder die Schule
oder Arbeitsstelle aufzusuchen. Die Covid-19-Pandemie und die damit einhergehenden Lockdowns
haben gezeigt, dass sich solche Bedürfnisse auch remote erfüllen lassen. Dennoch ist es
vielen Menschen ein Anliegen, dafür einen Ort aufzusuchen und außer Haus zu kommen, dabei
andere Menschen zu treffen und das Gefühl der Selbstständigkeit zu erfahren.
In ländlich geprägten und suburbanen Regionen werden die Mobilitätsbedürfnisse meist mit
dem eigenen Auto erfüllt. Neben dem Auto ist der Linienbus häufig die einzige Möglichkeit, um
in umliegende Gemeinden und Städte zu gelangen. Für viele Menschen ist der Linienbus jedoch
aufgrund der schlechten Taktung, fehlender Flexibilität und hoher Fahrpreise unattraktiv. Kurz
gesagt: Der Linienbus geht an den Bedürfnissen der Menschen vorbei!
Die hohe Zahl der Autos – in Deutschland sind aktuell rund 48 Millionen PKW zugelassen –
erfordert eine entsprechende Verkehrsinfrastruktur, wie Straßen und Parkflächen, und führt
zu einer großen Flächenversiegelung. Obwohl viel in die Infrastruktur für Autos investiert wird,
kommt diese vor allem in Städten regelmäßig an ihre Grenzen, was Staus zur Folge hat. Schon
heute steht jeder Autofahrer im Schnitt 46 Stunden pro Jahr im Stau (INRIX, 2020). Auch auf die
Umwelt und das Klima hat die hohe Zahl an Fahrzeugen negative Auswirkungen, unter anderem
aufgrund der Flächenversiegelung und der klimaschädlichen Emissionen. Um die Klimaziele
2030 zu erreichen, müssen allein im Verkehrssektor die Treibhausgasemissionen um knapp 50 %
reduziert werden.
In Großstädten geht daher der Trend dazu, Autos aus den Innenstädten herauszuhalten und die
freigewordenen Flächen für Wohnfläche, Plätze mit hoher Aufenthaltsqualität und als grüne
Oasen zu nutzen. Damit die Menschen dennoch zufriedenstellend von A nach B kommen,
müssen umweltfreundlichere Mobilitätsangebote ausgebaut werden. Der Bus ist solch ein
Mobilitätsangebot, das zur Mobilitätswende beitragen kann. Er befördert viele Menschen mit
vergleichsweise wenig Energie, benötigt keine extra Infrastruktur wie Schienen und ist damit
nachhaltiger als andere Transportmittel und vor allem deutlich nachhaltiger als ein Auto.
Besonders nachhaltig ist der Bus, wenn er gut ausgelastet ist. Jedoch steigen Menschen noch
lange nicht auf den Bus um, nur weil es ökologisch sinnvoll ist. Damit Menschen mit dem Bus
statt mit dem eigenen Auto fahren, muss der Busverkehr attraktiver werden, also die Bedürfnisse
der Menschen besser erfüllen.
Doch wie kann der Bus so attraktiv gestaltet werden, dass die Menschen ihr eigenes Auto
stehen lassen?
Verfasser: MHP
Das autonome Fahren wird die Landschaft an Mobilitätsangeboten, Akteuren und Partnerschaften, wie wir sie heute kennen, drastisch verändern. Die Nutzer:innen werden sich autonom fortbewegen und die gewonnene Zeit im Fahrzeug nutzen. Fahrzeughersteller werden eine weniger dominante Rolle in der Wertschöpfungskette einnehmen – was manche von ihnen zu reinen Hardware-Zulieferern transformiert. Die Relevanz einzelner Automobilmarken im Premium- und Volumensegment wird schwinden. Neue Marken werden entstehen. In diesem geänderten Gefüge verschieben sich die Umsatz- und Profit-Pools. Es ist davon auszugehen, dass die Marken führender digitaler Ökosysteme und Mobilitätsplattformen zukünftig mit Mobilität in Verbindung gebracht werden und den Großteil der Kund:innenschnittstelle für Mobilität besetzen. Heutige Fahrzeughersteller und deren Marken müssen sich damit arrangieren, dass sie für sich neue, erweiterte Rollen in bereits bestehenden Ökosystemen finden müssen. Vor allem Fahrzeughersteller und Flottenbetreiber müssen diesen Wandel proaktiv gestalten, um wirtschaftlich zu überleben. Die erforderlichen Kernkompetenzen müssen konsequent analysiert und mit den tatsächlichen Möglichkeiten im eigenen Unternehmen verglichen werden. Für Kompetenzen, die zu weit von den eigenen Möglichkeiten entfernt sind, müssen strategische Partnerschaften eingegangen werden, um erfolgreiche autonome Systeme zu realisieren. Grundsätzlich bedarf es robuster, zukunftsorientierter und durchaus mutiger Entscheidungen, um die notwendigen Entwicklungskompetenzen in traditionellen Unternehmen aufzubauen und vor allem die benötigten Software-Architekturen zu entwickeln. Insbesondere digitale Ökosysteme werden versuchen, mit der Integration von Mobilitätsdiensten in eigene Software-, Plattform- und Technologie-Infrastrukturen ihr Geschäftsmodell zu erweitern. Außerdem kann so die Loyalität der Nutzer:innen gesteigert und die verbrachte Zeit im eigenen Ökosystem maximiert werden. Neben der Stärkung des eigenen Angebots werden die eigenen Dienste über definierte Schnittstellen bereitgestellt, um die Integration in Fahrzeugarchitekturen und -systeme zu ermöglichen. U. a. hat Alphabet bereits damit begonnen, Mobilität digital über Google Maps konsumierbar und bezahlbar zu machen – ein weiterer Schritt in Richtung marktdominante Super-App. Politische Akteure und Behörden, die mit Investitionen in unterstützende Infrastruktur in den Bereichen Connectivity, Smart City oder Energy aufwarten, werden die Etablierung autonomer Mobilitäts- und Logistiksysteme fördern.
Verfasser: Bitkom
Die Technologie des automatisierten und vernetzten Fahrens entwickelt sich rasant. Einerseits eröffnet sie Lösungen und Anwendungsfälle für die Mobilitätswende, die vor wenigen Jahren noch in weit entfernter Zukunft schienen. Andererseits haben die Technologie und deren rasante Entwicklung massive Auswirkungen auf die Industrie und stellen diese vor dem Hintergrund ihrer digitalen Transformation vor große Her- ausforderungen: Die Automobilindustrie trifft auf neue, technologiegetriebene Player, die auf den Markt drängen und sie insbesondere im internationalen Kontext unter Druck setzen.
Doch nicht nur die Industrie und die Technologie selbst erfahren eine rasante Entwick- lung, auch die rechtlichen Rahmenbedingungen wurden in den letzten Jahren entschlossen und in hoher Geschwindigkeit vorangebracht: Deutschland und die Europäische Union haben mit ihren Rechtsrahmen zum autonomen Fahren eine sehr gute Basis geschaffen, die autonome Mobilität auf die Straße zu bringen – der regulatorische Rahmen ist ein starker Wettbewerbsvorteil gegenüber anderen Ländern der Welt. Allerdings geht es nun darum, diesen zügig und konsequent umzusetzen, um die Technologie schnellstmöglich auszurollen.
Dieses Whitepaper greift diese vielseitigen Chancen und Herausforderungen auf und stellt diese anhand konkreter Anwendungsfälle aus der Wirtschaft dar. Ziel ist es, einer- seits einen Überblick über verschiedene Lösungen und Anwendungen zu geben und diese damit greifbarer zu machen. Andererseits werden auf Basis der Anwendungsfälle jedoch insbesondere Handlungsempfehlungen für Politik und Wirtschaft abgeleitet.
Dabei beantwortet das Papier die folgenden Fragen:
■ Welche Möglichkeiten ergeben sich durch das autonome und vernetzte Fahren für den Personen- und Gütertransport?
■ Wie kann das autonome Fahren zur Mobilitätswende beitragen?
■ Vor welchen Herausforderungen stehen einzelne Akteure sowie auch die Branche
insgesamt?
■ Was kann die Politik tun, um den Roll-out der Technologie voranzutreiben und die
Anwendungsfälle auf die Straße zu bringen? Und was könnten Stakeholder selbst tun, um den Prozess sicher und effizient zu gestalten?
Schlussendlich möchte dieses Papier auch zu einem Dialog und zu einer verstärkten Zusammenarbeit zwischen Politik, Verwaltung, Wissenschaft, Technologie- und Mobilitätsunternehmen anregen.
Bayern ist Seilbahnland Nummer Eins in Deutschland. Allerdings gibt es bisher die Seilbahnen nur in den Alpen. Das soll sich jetzt ändern. Bayerns Verkehrsministerin will die Seilbahnen als unabhängiges Transportmittel auch in der Stadt einführen. Jetzt nutzt Aigner das Expertenwissen aus dem Bereich der schon existierenden Bergbahnen und führt es mit dem Fachwissen im Öffentlichen Nahverkehr (ÖPNV) zusammen. Interessierte Kommunen finden hier nicht nur Angaben zu rechtlichen Voraussetzungen, wie Genehmigungen oder technischer Planung. Auch über den Betrieb, die Finanzierung und über die stadtplanerischen Chancen können sich Kommunen jetzt informieren.
TwoGo ist eine mobile, cloudbasierte Lösung
für Unternehmen und Gemeinden, welche beim
Organisieren von Fahrgemeinschaften von
Mitarbeitern und Bürgern unterstützt.
Die Plattform ist speziell für die Bedürfnisse von
Pendlern, sowie für Dienstreisen entwickelt
worden. Die Verwendung von TwoGo fügt sich
nahtlos in den unternehmerischen und sozialen
Arbeitsalltag ein; gemeinsame Fahrten werden
automatisch, effizient und präzise vermittelt.
Automatisiertes Fahren wird aktuell auf allen Ebenen diskutiert. Dieses Open Access Buch greift das Thema aus Sicht des ÖPNV auf und stellt Chancen und Risiken des Einsatzes automatisierter Shuttlebusse im Nahverkehr dar. Am Beispiel Bad Birnbach/Niederbayern wird gezeigt, welche Herausforderungen bei der Einführung eines solchen Services zu erwarten sind und wie diese gelöst werden können. Dabei fokussiert sich das Buch auf die Vermittlung von im Feld erhobenen Daten, z.B. zu technischen Schwierigkeiten, Erfahrungsberichten von Anwohnern und Gästen, Akzeptanz in der Bevölkerung, infrastrukturellen Anforderungen, etc. Konkrete Handlungsempfehlungen für Städteplaner, ÖPNV-Betreiber/-Strategen oder Kommunen, die eine Einführung automatisierter Busse in Erwägung ziehen, runden das Werk ab.
Dieses Buch wird unter der Creative Commons Namensnennung 4.0 International Lizenz (http:// creativecommons.org/licenses/by/4.0/deed.de) veröffentlicht. Hrsg. Andreas Riener, Alexandra Appel, Wolfgang Dorner, Thomas Huber, Jan Christopher Kolb, Harry Wagner
(31.1.2020) Oberbürgermeister Dieter Reiter hat heute gemeinsam mit Stadtbaurätin Elisabeth
Merk einen Gesamtplan für Münchens Mobilität in den nächsten Jahrzehnten vorgestellt.
Wo könnten neue U- und Trambahnlinien entstehen, wo die neuen Radschnellwege
verlaufen und wo kann ich vom Auto auf umweltbewusste Verkehrsmittel umsteigen? Die
Mobilität von Morgen wird dabei geprägt sein von einem optimalen Ineinandergreifen verschiedener
Mobilitätsformen – allen voran einem optimierten und breit ausgebauten Öffentlichen
Nahverkehr, einem breiten Radwegenetz, neuen Angeboten für Pendler*innen
in sogenannten HOV-Lanes (Spuren für Fahrzeuge mit mehreren Insassen), eigenen
Busspuren und cleveren Mobilitätskonzepten bei der Siedlungsentwicklung mit innovativen
Verkehrsmitteln.
Vorlesung "Social Media Channels" im Rahmen des berufsbegleitenden Studiengangs zum Social Media Manager (2014) an der VWA Freiburg. Die aktuelle Landschaft von Social Media Kanälen wird vorgestellt (Twitter, YouTube, Snapchat, Instagram, Tumblr, XING, LinkedIn) und aktuelle Trends werden besprochen (#Selfie #Memes #Virals).
Top 10 im Lebensmitteleinzelhandel in Deutschland im Jahr 2013 gemessen am Gesamtumsatz (brutto) im Jahr 2012. Quelle: TradeDimensions/Lebensmittel Zeitung
Zum Anlass der ANUGA 2011 haben der Bundesverband des Deutschen
Lebensmittelhandels (BVL) und die Kölnmesse ein Szenario-Projekt zur
Zukunft unserer Lebensmittel initiiert.
Surat Digital Marketing School is created to offer a complete course that is specifically designed as per the current industry trends. Years of experience has helped us identify and understand the graduate-employee skills gap in the industry. At our school, we keep up with the pace of the industry and impart a holistic education that encompasses all the latest concepts of the Digital world so that our graduates can effortlessly integrate into the assigned roles.
This is the place where you become a Digital Marketing Expert.
This tutorial presentation provides a step-by-step guide on how to use Facebook, the popular social media platform. In simple and easy-to-understand language, this presentation explains how to create a Facebook account, connect with friends and family, post updates, share photos and videos, join groups, and manage privacy settings. Whether you're new to Facebook or just need a refresher, this presentation will help you navigate the features and make the most of your Facebook experience.
Your LinkedIn Success Starts Here.......SocioCosmos
In order to make a lasting impression on your sector, SocioCosmos provides customized solutions to improve your LinkedIn profile.
https://www.sociocosmos.com/product-category/linkedin/
Exploring The Dimensions and Dynamics of Felt Obligation: A Bibliometric Anal...AJHSSR Journal
ABSTARCT: This study presents, to our knowledge, the first bibliometric analysis focusing on the concept of
"felt obligation," examining 120 articles published between 1986 and 2024. The aim of the study is to deepen our
understanding of the existing knowledge in the field of "felt obligation" and to provide guidance for further
research. The analysis is centered around the authors, countries, institutions, and keywords of the articles. The
findings highlight prominent researchers in this field, leading universities, and influential journals. Particularly,
it is identified that China plays a leading role in "felt obligation" research. The analysis of keywords emphasizes
the thematic focuses of these studies and provides a roadmap for future research. Finally, various
recommendations are presented to deepen the knowledge in this area and promote applied research. This study
serves as a foundation to expand and advance the understanding of "felt obligation" in the field.
KEYWORDS: Felt Obligation, Bibliometric Analysis, Research Trends
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Tumblr 2014 - statistical overview and comparison with popular social services
1. arXiv:1403.5206v1[cs.SI]20Mar2014
What is Tumblr: A Statistical Overview and Comparison
Yi Chang, Lei Tang, Yoshiyuki Inagaki and Yan Liu
Yahoo! Labs, Sunnyvale, CA 94089, USA
yichang@yahoo-inc.com,leitang@acm.org,
inagakiy@yahoo-inc.com,yanliu.cs@usc.edu
Abstract
Tumblr, as one of the most popular microblogging platforms,
has gained momentum recently. It is reported to have 166.4
millions of users and 73.4 billions of posts by January 2014.
While many articles about Tumblr have been published in
major press, there is not much scholar work so far. In this pa-
per, we provide some pioneer analysis on Tumblr from a va-
riety of aspects. We study the social network structure among
Tumblr users, analyze its user generated content, and describe
reblogging patterns to analyze its user behavior. We aim to
provide a comprehensive statistical overview of Tumblr and
compare it with other popular social services, including blo-
gosphere, Twitter and Facebook, in answering a couple of key
questions: What is Tumblr? How is Tumblr different from
other social media networks? In short, we find Tumblr has
more rich content than other microblogging platforms, and
it contains hybrid characteristics of social networking, tradi-
tional blogosphere, and social media. This work serves as an
early snapshot of Tumblr that later work can leverage.
Introduction
Tumblr, as one of the most prevalent microblogging sites,
has become phenomenal in recent years, and it is acquired
by Yahoo! in 2013. By mid-January 2014, Tumblr has 166.4
millions of users and 73.4 billions of posts1
. It is reported to
be the most popular social site among young generation, as
half of Tumblr’s visitor are under 25 years old2
. Tumblr is
ranked as the 16th most popular sites in United States, which
is the 2nd most dominant blogging site, the 2nd largest mi-
croblogging service, and the 5th most prevalent social site3
.
In contrast to the momentum Tumblr gained in recent press,
little academic research has been conducted over this bur-
geoning social service. Naturally questions arise: What is
Tumblr? What is the difference between Tumblr and other
blogging or social media sites?
Traditional blogging sites, such as Blogspot4
and Living-
Social5
, have high quality content but little social interac-
tions. Nardi et al. (Nardi et al. 2004) investigated blogging
as a form of personal communication and expression, and
1
http://www.tumblr.com/about
2
http://www.webcitation.org/64UXrbl8H
3
http://www.alexa.com/topsites/countries/US
4
http://blogspot.com
5
http://livesocial.com
showed that the vast majority of blog posts are written by
ordinary people with a small audience. On the contrary, pop-
ular social networking sites like Facebook6
, have richer so-
cial interactions, but lower quality content comparing with
blogosphere. Since most social interactions are either un-
published or less meaningful for the majority of public audi-
ence, it is natural for Facebook users to form different com-
munities or social circles. Microblogging services, in be-
tween of traditional blogging and online social networking
services, have intermediate quality content and intermediate
social interactions. Twitter7
, which is the largest microblog-
ging site, has the limitation of 140 characters in each post,
and the Twitter following relationship is not reciprocal: a
Twitter user does not need to follow back if the user is fol-
lowed by another. As a result, Twitter is considered as a new
social media (Kwak et al. 2010), and short messages can be
broadcasted to a Twitter user’s followers in real time.
Tumblr is also posed as a microblogging platform. Tum-
blr users can follow another user without following back,
which forms a non-reciprocal social network; a Tumblr post
can be re-broadcasted by a user to its own followers via re-
blogging. But unlike Twitter, Tumblr has no length limi-
tation for each post, and Tumblr also supports multimedia
post, such as images, audios or videos. With these differ-
ences in mind, are the social network, user generated con-
tent, or user behavior on Tumblr dramatically different from
other social media sites?
In this paper, we provide a statistical overview over Tum-
blr from assorted aspects. We study the social network struc-
ture among Tumblr users and compare its network proper-
ties with other commonly used ones. Meanwhile, we study
content generated in Tumblr and examine the content gen-
eration patterns. One step further, we also analyze how a
blog post is being reblogged and propagated through a net-
work, both topologically and temporally. Our study shows
that Tumblr provides hybrid microblogging services: it con-
tains dual characteristics of both social media and traditional
blogging. Meanwhile, surprising patterns surface. We de-
scribe these intriguing findings and provide insights, which
hopefully can be leveraged by other researchers to under-
stand more about this new form of social media.
6
http://facebook.com
7
http://twitter.com
2. Tumblr at First Sight
Tumblr is ranked the second largest microblogging service,
right after Twitter, with over 166.4 million users and 73.4
billion posts by January 2014. Tumblr is easy to register,
and one can sign up for Tumblr service with a valid email
address within 30 seconds. Once sign in Tumblr, a user can
follow other users. Different from Facebook, the connec-
tions in Tumblr do not require mutual confirmation. Hence
the social network in Tumblr is unidirectional.
Both Twitter and Tumblr are considered as microblogging
platforms. Comparing with Twitter, Tumblr exposes several
differences:
• There is no length limitation for each post;
• Tumblr supports multimedia posts, such as images, audios
and videos;
• Similar to hashtags in Twitter, bloggers can also tag their
blog post, which is commonplace in traditional blog-
ging. But tags in Tumblr are seperate from blog content,
while in Twitter the hashtag can appear anywhere within
a tweet.
• Tumblr recently (Jan. 2014) allowed users to mention and
link to specific users inside posts. This @user mechanism
needs more time to be adopted by the community;
• Tumblr does not differentiate verified account.
Figure 1: Post Types in Tumblr
Specifically, Tumblr defines 8 types of posts: photo, text,
quote, audio, video, chat, link and answer. As shown in
Figure 1, one has the flexibility to start a post in any type ex-
cept answer. Text, photo, audio, video and link allow one to
post, share and comment any multimedia content. Quote and
chat, which are not available in most other social network-
ing platforms, let Tumblr users share quote or chat history
from ichat or msn. Answer occurs only when one tries to
interact with other users: when one user posts a question, in
particular, writes a post with text box ending with a question
mark, the user can enable the option for others to answer the
question, which will be disabled automatically after 7 days.
A post can also be reblogged by another user to broadcast to
his own followers. The reblogged post will quote the origi-
nal post by default and allow the reblogger to add additional
comments.
Figure 2 demonstrates the distribution of Tumblr post
types, based on 586.4 million posts we collected. As seen
in the figure, even though all kinds of content are sup-
ported, photo and text dominate the distribution, accounting
for more than 92% of the posts. Therefore, we will con-
centrate on these two types of posts for our content analysis
later.
Since Tumblr has a strong presence of photos, it is natural
to compare it to other photo or image based social networks
Photo: 78.11%
Text: 14.13%
Quote: 2.27%
Audio: 2.01%
Video: 1.35%
Chat: 0.85%
Answer: 0.82%
Link: 0.46%
Figure 2: Distribution of Posts (Better viewed in color)
like Flickr8
and Pinterest9
. Flickr is mainly an image host-
ing website, and Flicker users can add contact, comment or
like others’ photos. Yet, different from Tumblr, one cannot
reblog another’s photo in Flickr. Pinterest is designed for
curators, allowing one to share photos or videos of her taste
with the public. Pinterest links a pin to the commercial web-
site where the product presented in the pin can be purchased,
which accounts for a stronger e-commerce behavior. There-
fore, the target audience of Tumblr and Pinterest are quite
different: the majority of users in Tumblr are under age 25,
while Pinterest is heavily used by women within age from
25 to 44 (Mittal et al. 2013).
We directly sample a sub-graph snapshot of social net-
work from Tumblr on August 2013, which contains 62.8
million nodes and 3.1 billion edges. Though this graph is
not yet up-to-date, we believe that many network proper-
ties should be well preserved given the scale of this graph.
Meanwhile, we sample about 586.4 million of Tumblr posts
from August 10 to September 6, 2013. Unfortunately, Tum-
blr does not require users to fill in basic profile information,
such as gender or location. Therefore, it is impossible for us
to conduct user profile analysis as done in other works. In
order to handle such large volume of data, most statistical
patterns are computed through a MapReduce cluster, with
some algorithms being tricky. We will skip the involved im-
plementation details but concentrate solely on the derived
patterns.
Most statistical patterns can be presented in three dif-
ferent forms: probability density function (PDF), cumula-
tive distribution function (CDF) or complementary cumula-
tive distribution function (CCDF), describing Pr(X = x),
Pr(X ≤ x) and Pr(X ≥ x) respectively, where X is a
random variable and x is certain value. Due to the space
limit, it is impossible to include all of them. Hence, we de-
cide which form(s) to include depending on presentation and
comparison convenience with other relevant papers. That is,
if CCDF is reported in a relevant paper, we try to also report
CCDF here so that rigorous comparison is possible.
Next, we study properties of Tumblr through different
8
http://flickr.com
9
http://pinterest.com
3. 10
0
10
2
10
4
10
6
10
8
10
−8
10
−6
10
−4
10
−2
10
0
In−Degree or Out−Degree
CCDF
In−Degree
Out−Degree
(a) in/out degree distribution
0 1
2
3
4
5
6
7
8
9
10
0
1
2
3
4
5
6
7
8
9
10
0
0.05
0.1
0.15
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Out−Degree = 2YIn−Degree = 2
X
PercentageofUsers
(b) in/out degree correlation
10
0
10
1
10
2
10
3
10
4
10
5
10
−8
10
−6
10
−4
10
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10
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In−Degree (same to Out−Degree)
CCDF
(c) degree distribution in r-graph
Figure 3: Degree Distribution of Tumblr Network
lenses, in particular, as a social network, a content gener-
ation website, and an information propagation platform, re-
spectively.
Tumblr as Social Network
We begin our analysis of Tumblr by examining its social
network topology structure. Numerous social networks
have been analyzed in the past, such as traditional blo-
gosphere (Shi et al. 2007), Twitter (Java et al. 2007;
Kwak et al. 2010), Facebook (Ugander et al. 2011), and
instant messenger communication network (Leskovec and
Horvitz 2008). Here we run an array of standard network
analysis to compare with other networks, with results sum-
marized in Table 110
.
Degree Distribution. Since Tumblr does not require mu-
tual confirmation when one follows another user, we repre-
sent the follower-followee network in Tumblr as a directed
graph: in-degree of a user represents how many follow-
ers the user has attracted, while out-degree indicates how
many other users one user has been following. Our sampled
sub-graph contains 62.8 million nodes and 3.1 billion edges.
Within this social graph, 41.40% of nodes have 0 in-degree,
and the maximum in-degree of a node is 4.06 million. By
contrast, 12.74% of nodes have 0 out-degree, the maximum
out-degree of a node is 155.5k. Top popular Tumblr users
include equipo11
, instagram12
, and woodendreams13
. This
indicates the media characteristic of Tumblr: the most pop-
ular user has more than 4 million audience, while more than
40% of users are purely audience since they don’t have any
followers.
10
Even though we wish to include results over other popular
social media networks like Pinterest, Sina Weibo and Instagram,
analysis over those websites not available or just small-scale case
studies that are difficult to generalize to a comprehensive scale for
a fair comparison. Actually in the Table, we observe quite a dis-
crepancy between numbers reported over a small twitter data set
and another comprehensive snapshot.
11
http://equipo.tumblr.com
12
http://instagram.tumblr.com
13
http://woodendreams.tumblr.com
Figure 3(a) demonstrates the distribution of in-degrees in
the blue curve and that of out-degrees in the red curve, where
y-axis refers to the cumulated density distribution function
(CCDF): the probability that accounts have at least k in-
degrees or out-degrees, i.e., P(K >= k). It is observed
that Tumblr users’ in-degree follows a power-law distribu-
tion with exponent −2.19, which is quite similar from the
power law exponent of Twitter at −2.28 (Kwak et al. 2010)
or that of traditional blogs at −2.38 (Shi et al. 2007). This
also confirms with earlier empirical observation that most
social network have a power-law exponent between −2 and
−3 (Clauset, Shalizi, and Newman 2007).
In regard to out-degree distribution, we notice the red
curve has a big drop when out-degree is around 5000, since
there was a limit that ordinary Tumblr users can follow at
most 5000 other users. Tumblr users’ out-degree does not
follow a power-law distribution, which is similar to blogo-
sphere of traditional blogging (Shi et al. 2007).
If we explore user’s in-degree and out-degree together, we
could generate normalized 3-D histogram in Figure 3(b). As
both in-degree and out-degree follow the heavy-tail distri-
bution, we only zoom in those user who have less than 210
in-degrees and out-degrees. Apparently, there is a positive
correlation between in-degree and out-degree because of the
dominance of diagonal bars. In aggregation, a user with low
in-degree tends to have low out-degree as well, even though
some nodes, especially those top popular ones, have very
imbalanced in-degree and out-degree.
Reciprocity. Since Tumblr is a directed network, we
would like to examine the reciprocity of the graph. We de-
rive the backbone of the Tumblr network by keeping those
reciprocal connections only, i.e., user a follows b and vice
versa. Let r-graph denote the corresponding reciprocal
graph. We found 29.03% of Tumblr user pairs have reci-
procity relationship, which is higher than 22.1% of reci-
procity on Twitter (Kwak et al. 2010) and 3% of reciprocity
on Blogosphere (Shi et al. 2007), indicating a stronger in-
teraction between users in the network. Figure 3(c) shows
the distribution of degrees in the r-graph. There is a turning
point due to the Tumblr limit of 5000 followees for ordi-
nary users. The reciprocity relationship on Tumblr does not
4. Table 1: Comparison of Tumblr with other popular social networks. The numbers of Blogosphere, Twitter-small, Twitter-
huge, Facebook, and MSN are obtained from (Shi et al. 2007; Java et al. 2007; Kwak et al. 2010; Ugander et al. 2011;
Leskovec and Horvitz 2008), respectively. In the table, – implies the corresponding statistic is not available or not applicable;
GCC denotes the giant connected component; the symbols in parenthesis m, d, e, r respectively represent mean, median, the
90% effective diameter, and diameter (the maximum shortest path in the network).
Metric Tumblr Blogosphere Twitter-small Twitter-huge Facebook MSN
#nodes 62.8M 143,736 87,897 41.7M 721M 180M
#links 3.1B 707,761 829,467 1.47B 68.7B 1.3B
in-degree distr ∝ k−2.19
∝ k−2.38
∝ k−2.4
∝ k−2.276
– –
degree distr in r-graph = power-law – – – = power-law ∝ k0.8
e−0.03k
direction directed directed directed directed undirected undirected
reciprocity 29.03% 3% 58% 22.1% – –
degree correlation 0.106 – – > 0 0.226 –
avg distance 4.7(m), 5(d) 9.3(m) – 4.1(m), 4(d) 4.7(m), 5(d) 6.6(m), 6(d)
diameter 5.4(e), ≥ 29(r) 12(r) 6(r) 4.8(e), ≥ 18(r) < 5(e) 7.8(e), ≥ 29(r)
GCC coverage 99.61% 75.08% 93.03% – 99.91% 99.90%
follow the power law distribution, since the curve mostly is
convex, similar to the pattern reported over Facebook(Ugan-
der et al. 2011).
Meanwhile, it has been observed that one’s degree is cor-
related with the degree of his friends. This is also called
degree correlation or degree assortativity (Newman 2002;
2003). Over the derived r-graph, we obtain a correlation of
0.106 between terminal nodes of reciprocate connections,
reconfirming the positive degree assortativity as reported in
Twitter (Kwak et al. 2010). Nevertheless, compared with
the strong social network Facebook, Tumblr’s degree assor-
tativity is weaker (0.106 vs. 0.226).
Degree of Separation. Small world phenomenon is al-
most universal among social networks. With this huge Tum-
blr network, we are able to validate the well-known “six de-
grees of separation” as well. Figure 4 displays the distribu-
tion of the shortest paths in the network. To approximate the
distribution, we randomly sample 60,000 nodes as seed and
calculate for each node the shortest paths to other nodes. It
is observed that the distribution of paths length reaches its
mode with the highest probability at 4 hops, and has a me-
dian of 5 hops. On average, the distance between two con-
nected nodes is 4.7. Even though the longest shortest path
in the approximation has 29 hops, 90% of shortest paths are
within 5.4 hops. All these numbers are close to those re-
ported on Facebook and Twitter, yet significantly smaller
than that obtained over blogosphere and instant messenger
network (Leskovec and Horvitz 2008).
Component Size. The previous result shows that those
users who are connected have a small average distance. It
relies on the assumption that most users are connected to
each other, which we shall confirm immediately. Because
the Tumblr graph is directed, we compute out all weakly-
connected components by ignoring the direction of edges.
It turns out the giant connected component (GCC) encom-
passes 99.61% of nodes in the graph. Over the derived r-
graph, 97.55% are residing in the corresponding GCC. This
finding suggests the whole graph is almost just one con-
nected component, and almost all users can reach others
through just few hops.
0 5 10 15 20 25 30
10
−10
10
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10
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10
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10
0
Shortest Path Length
PDF
0 5 10 15 20 25 30
0
0.2
0.4
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1
CDF
Shortest Path Length
Figure 4: Shortest Path Distribution
To give a palpable understanding, we summarize com-
monly used network statistics in Table 1. Those numbers
from other popular social networks (blogosphere, Twitter,
Facebook, and MSN) are also included for comparison.
From this compact view, it is obvious traditional blogs yield
a significantly different network structure. Tumblr, even
though originally proposed for blogging, yields a network
structure that is more similar to Twitter and Facebook.
Tumblr as Blogosphere for
Content Generation
As Tumblr is initially proposed for the purpose of blogging,
here we analyze its user generated contents. As described
earlier, photo and text posts account for more than 92% of
total posts. Hence, we concentrate only on these two types
5. Text Post Photo Caption
Dataset Dataset
# Posts 21.5 M 26.3 M
Mean Post Length 426.7 Bytes 64.3 Bytes
Median Post Length 87 Bytes 29 Bytes
Max Post Length 446.0 K Bytes 485.5 K Bytes
Table 2: Statistics of User Generated Contents
of posts. One text post may contain URL, quote or raw mes-
sage. In this study, we are mainly interested in the authentic
contents generated by users. Hence, we extract raw mes-
sages as the content information of each text post, by re-
moving quotes and URLs. Similarly, photo posts contains 3
categories of information: photo URL, quote photo caption,
raw photo caption. While the photo URL might contain lots
of additional meta information, it would require tremendous
effort to analyze all images in Tumblr. Hence, we focus on
raw photo captions as the content of each photo post. We
end up with two datasets of content: one is text post, and the
other is photo caption.
What’s the effect of no length limit for post? Both
Tumblr and Twitter are considered microblogging platforms,
yet there is one key difference: Tumblr has no length limit
while Twitter enforces the strict limitation of 140 bytes for
each tweet. How does this key difference affect user post
behavior?
It has been reported that the average length of posts on
Twitter is 67.9 bytes and the median is 60 bytes14
. Corre-
sponding statistics of Tumblr are shown in Table 2. For the
text post dataset, the average length is 426.7 bytes and the
median is 87 bytes, which both, as expected, are longer than
that of Twitter. Keep in mind Tumblr’s numbers are obtained
after removing all quotes, photos and URLs, which further
discounts the discrepancy between Tumblr and Twitter. The
big gap between mean and median is due to a small per-
centage of extremely long posts. For instance, the longest
text post is 446K bytes in our sampled dataset. As for photo
captions, naturally we expect it to be much shorter than text
posts. The average length is around 64.3 bytes, but the me-
dian is only 29 bytes. Although photo posts are dominant in
Tumblr, the number of text posts and photo captions in Ta-
ble 2 are comparable, because majority of photo posts don’t
contain any raw photo captions.
A further related question: is the 140-byte limit sensible?
We plot post length distribution of the text post dataset, and
zoom into less than 280 bytes in Figure 5. About 24.48%
of posts are beyond 140 bytes, which indicates that at least
around one quarter of posts will have to be rewritten in a
more compact version if the limit was enforced in Tumblr.
Blending all numbers above together, we can see at least
two types of posts: one is more like posting a reference
(URL or photo) with added information or short comments,
the other is authentic user generated content like in tradi-
tional blogging. In other words, Tumblr is a mix of both
14
http://www.quora.com/Twitter-1/What-is-the-average-length-
of-a-tweet
0 50 100 150 200 250 300
0
0.2
0.4
0.6
0.8
1
Post Length (Bytes)
CCDF
Figure 5: Post Length Distribution
Topic Topical Keywords
Pop music song listen iframe band album lyrics
Music video guitar
Sports game play team win video cookie
ball football top sims fun beat league
Internet internet computer laptop google search online
site facebook drop website app mobile iphone
Pets big dog cat animal pet animals bear tiny
small deal puppy
Medical anxiety pain hospital mental panic cancer
depression brain stress medical
Finance money pay store loan online interest buying
bank apply card credit
Table 3: Topical Keywords from Text Post Dataset
types of posts, and its no-length-limit policy encourages its
users to post longer high-quality content directly.
What are people talking about? Because there is no
length limit on Tumblr, the blog post tends to be more
meaningful, which allows us to run topic analysis over the
two datasets to have an overview of the content. We run
LDA (Blei, Ng, and Jordan: 2003) with 100 topics on both
datasets, and showcase several topics and their correspond-
ing keywords on Tables 3 and 4, which also show the high
quality of textual content on Tumblr clearly. Medical, Pets,
Pop Music, Sports are shared interests across 2 different
datasets, although representative topical keywords might be
different even for the same topic. Finance, Internet only
attracts enough attentions from text posts, while only signif-
icant amount of photo posts show interest to Photography,
Scenery topics. We want to emphasize that most of these
keywords are semantically meaningful and representative of
the topics.
Who are the major contributors of contents? There are
two potential hypotheses. 1) One supposes those socially
popular users post more. This is derived from the result that
those popular users are followed by many users, therefore
blogging is one way to attract more audience as followers.
Meanwhile, it might be true that blogging is an incentive for
celebrities to interact or reward their followers. 2) The other
assumes that long-term users (in terms of registration time)
post more, since they are accustomed to this service, and
they are more likely to have their own focused communities
6. Topic Topical Keywords
Pets cat dog cute upload kitty batch puppy
pet animal kitten adorable
Scenery summer beach sun sky sunset sea nature
ocean island clouds lake pool beautiful
Pop music song rock band album listen lyrics
Music punk guitar dj pop sound hip
Photography photo instagram pic picture check
daily shoot tbt photography
Sports team world ball win football club
round false soccer league baseball
Medical body pain skin brain depression hospital
teeth drugs problems sick cancer blood
Table 4: Topical Keywords from Photo Caption Dataset
or social circles. These peer interactions encourage them to
generate more authentic content to share with others.
Do socially popular users or long-term users generate
more contents? In order to answer this question, we choose
a fixed time window of two weeks in August 2013 and ex-
amine how frequent each user blogs on Tumblr. We sort all
users based on their in-degree (or duration time since reg-
istration) and then partition them into 10 equi-width bins.
For each bin, we calculate the average blogging frequency.
For easy comparison, we consider the maximal value of all
bins as 1, and normalize the relative ratio for other bins.
The results are displayed in Figure 6, where x-axis from left
to right indicates increasing in-degree (or decreasing dura-
tion time). For brevity, we just show the result for text post
dataset as similar patterns were observed over photo cap-
tions.
The patterns are strong in both figures. Those users who
have higher in-degree tend to post more, in terms of both
mean and median. One caveat is that what we observe
and report here is merely correlation, and it does not de-
rive causality. Here we draw a conservative conclusion that
the social popularity is highly positively correlated with user
blog frequency. A similar positive correlation is also ob-
served in Twitter(Kwak et al. 2010).
In contrast, the pattern in terms of user registration time
is beyond our imagination until we draw the figure. Sur-
prisingly, those users who either register earliest or register
latest tend to post less frequently. Those who are in between
are inclined to post more frequently. Obviously, our initial
hypothesis about the incentive for new users to blog more is
invalid. There could be different explanations in hindsight.
Rather than guessing the underlying explanation, we decide
to leave this phenomenon as an open question to future re-
searchers.
As for reference, we also look at average post-length of
users, because it has been adopted as a simple metric to ap-
proximate quality of blog posts (Agarwal et al. 2008). The
corresponding correlations are plot in Figure 7. In terms of
post length, the tail users in social networks are the winner.
Meanwhile, long-term or recently-joined users tend to post
longer blogs. Apparently, this pattern is exactly opposite to
post frequency. That is, the more frequent one blogs, the
In−Degree from Low to High along x−Axis
0
0.2
0.4
0.6
0.8
1
1.2
NormalizedPostFrequency
Mean of Post Frequency
Median of Post Frequency
Registration Time from Early to Late along x−Axis
0
0.2
0.4
0.6
0.8
1
1.2
NormalizedPostFrequency
Mean of Post Frequency
Median of Post Frequency
Figure 6: Correlation of Post Frequency with User In-degree
or Duration Time since Registration
shorter the blog post is. And less frequent bloggers tend to
have longer posts. That is totally valid considering each in-
dividual has limited time and resources. We even changed
the post length to the maximum for each individual user
rather than average, but the pattern remains still.
In summary, without the post length limitation, Tumblr
users are inclined to write longer blogs, and thus leading to
higher-quality user generated content, which can be lever-
aged for topic analysis. The social celebrities (those with
large number of followers) are the main contributors of con-
tents, which is similar to Twitter (Wu et al. 2011). Surpris-
ingly, long-term users and recently-registered users tend to
blog less frequently. The post-length in general has a neg-
ative correlation with post frequency. The more frequently
one posts, the shorter those posts tend to be.
Tumblr for Information Propagation
Tumblr offers one feature which is missing in traditional
blog services: reblog. Once a user posts a blog, other
users in Tumblr can reblog to comment or broadcast to their
own followers. This enables information to be propagated
through the network. In this section, we examine the reblog-
7. In−Degree from Low to High along x−Axis
0
0.2
0.4
0.6
0.8
1
1.2
NormalizedPostLength Mean of Post Length
Median of Post Length
Registration Time from Early to Late along x−Axis
0
0.2
0.4
0.6
0.8
1
1.2
NormalizedPostLength
Mean of Post Length
Median of Post Length
Figure 7: Correlation of Post Length with User In-degree or
Duration Time since Registration
ging patterns in Tumblr. We examine all blog posts uploaded
within the first 2 weeks, and count reblog events in the sub-
sequent 2 weeks right after the blog is posted, so that there
would be no bias because of the time window selection in
our blog data.
Who are reblogging? Firstly, we would like to under-
stand which users tend to reblog more? Those people who
reblog frequently serves as the information transmitter. Sim-
ilar to the previous section, we examine the correlation of
reblogging behavior with users’ in-degree. As shown in the
Figure 8, social celebrities, who are the major source of con-
tents, reblog a lot more compared with other users. This re-
blogging is propagated further through their huge number
of followers. Hence, they serve as both content contributor
and information transmitter. On the other hand, users who
registered earlier reblog more as well. The socially popu-
lar and long-term users are the backbone of Tumblr network
to make it a vibrant community for information propagation
and sharing.
Reblog size distribution. Once a blog is posted, it can be
reblogged by others. Those reblogs can be reblogged even
further, which leads to a tree structure, which is called reblog
cascade, with the first author being the root node. The reblog
In−Degree from Low to High along x−Axis
0
0.2
0.4
0.6
0.8
1
1.2
NormalizedReblogFrequency
Mean of Reblog Frequency
Median of Reblog Frequency
Registration Time from Early to Late along x−Axis
0
0.2
0.4
0.6
0.8
1
1.2
NormalizedReblogFrequency
Mean of Reblog Frequency
Median of Reblog Frequency
Figure 8: Correlation of Reblog Frequency with User In-
degree or Duration Time since Registration
cascade size indicates the number of reblog actions that have
been involved in the cascade. Figure 9 plots the distribution
of reblog cascade sizes. Not surprisingly, it follows a power-
law distribution, with majority of reblog cascade involving
few reblog events. Yet, within a time window of two weeks,
the maximum cascade could reach 116.6K. In order to have
a detailed understanding of reblog cascades, we zoom into
the short head and plot the CCDF up to reblog cascade size
equivalent to 20 in Figure 9. It is observed that only about
19.32% of reblog cascades have size greater than 10. By
contrast, only 1% of retweet cascades have size larger than
10 (Kwak et al. 2010). The reblog cascades in Tumblr tend
to be larger than retweet cascades in Twitter.
Reblog depth distribution. As shown in previous sec-
tions, almost any pair of users are connected through few
hops. How many hops does one blog to propagate to another
user in reality? Hence, we look at the reblog cascade depth,
the maximum number of nodes to pass in order to reach one
leaf node from the root node in the reblog cascade structure.
Note that reblog depth and size are different. A cascade of
depth 2 can involve hundreds of nodes if every other node in
the cascade reblogs the same root node.
Figure 10 plots the distribution of number of hops: again,
8. 10
0
10
2
10
4
10
6
10
−8
10
−6
10
−4
10
−2
10
0
Reblog Cascade Size
PDF
0 5 10 15 20 25
0
0.2
0.4
0.6
0.8
1
Reblog Cascade Size
CCDF
Figure 9: Distribution of Reblog Cascade Size
the reblog cascade depth distribution follows a power law as
well according to the PDF; when zooming into the CCDF,
we observe that only 9.21% of reblog cascades have depth
larger than 6. That is, majority of cascades can reach just
few hops, which is consistent with the findings reported over
Twitter (Bakshy et al. 2011). Actually, 53.31% of cas-
cades in Tumblr have depth 2. Nevertheless, the maximum
depth among all cascades can reach 241 based on two week
data. This looks unlikely at first glimpse, considering any
two users are just few hops away. Indeed, this is because
users can add comment while reblogging, and thus one user
is likely to involve in one reblog cascade multiple times. We
notice that some Tumblr users adopt reblog as one way for
conversation or chat.
Reblog Structure Distribution. Since most reblog cas-
cades are few hops, here we show the cascade tree structure
distribution up to size 5 in Figure 11. The structures are
sorted based on their coverage. Apparently, a substantial
percentage of cascades (36.05%) are of size 2, i.e., a post
being reblogged merely once. Generally speaking, a reblog
cascade of a flat structure tends to have a higher probabil-
ity than a reblog cascade of the same size but with a deep
structure. For instance, a reblog cascade of size 3 have two
variants, of which the flat one covers 9.42% cascade while
the deep one drops to 5.85%. The same patten applies to
reblog cascades of size 4 and 5. In other words, it is easier
to spread a message widely rather than deeply in general.
This implies that it might be acceptable to consider only the
cascade effect under few hops and focus those nodes with
larger audience when one tries to maximize influence or in-
formation propagation.
Temporal patten of reblog. We have investigated the
information propagation spatially in terms of network topol-
10
0
10
1
10
2
10
3
10
−8
10
−6
10
−4
10
−2
10
0
Reblog Cascade Depth
PDF
0 5 10 15 20 25
0
0.2
0.4
0.6
0.8
1
Reblog Cascade Depth
CCDF
Figure 10: Distribution of Reblog Cascade Depth
1m 10m 1h 1d 1w
0
0.2
0.4
0.6
0.8
1
CDF
Lag Time of First Reblog
Figure 12: Distribution of Time Lag between a Blog and its
first Reblog
ogy, now we study how fast for one blog to be reblogged?
Figure 12 displays the distribution of time gap between a
post and its first reblog. There is a strong bias toward re-
cency. The larger the time gap since a blog is posted, the
less likely it would be reblogged. 75.03% of first reblog ar-
rive within the first hour since a blog is posted, and 95.84%
of first reblog appears within one day. Comparatively, It has
been reported that “half of retweeting occurs within an hour
and 75% under a day” (Kwak et al. 2010) on Twitter. In
short, Tumblr reblog has a strong bias toward recency, and
information propagation on Tumblr is fast.
Related Work
There are rich literatures on both existing and emerging on-
line social network services. Statistical patterns across dif-
ferent types of social networks are reported, including tradi-
tional blogosphere (Shi et al. 2007), user-generated content
platforms like Flickr, Youtube and LiveJournal (Mislove et
al. 2007), Twitter (Java et al. 2007; Kwak et al. 2010),
9. 36.05% 9.42% 5.85% 3.58% 2.78%1.69% 1.44% 1.20% 1.15% 0.58% 0.51% 0.42% 0.33% 0.31% 0.24% 0.21%
Figure 11: Cascade Structure Distribution up to Size 5. The percentage at the top is the coverage of cascade structure.
instant messenger network (Leskovec and Horvitz 2008),
Facebook (Ugander et al. 2011), and Pinterest (Gilbert et
al. 2013; Ottoni et al. 2013). Majority of them observe
shared patterns such as long tail distribution for user de-
grees (power law or power law with exponential cut-off),
small (90% quantile effective) diameter, positive degree as-
sociation, homophily effect in terms of user profiles (age or
location), but not with respect to gender. Indeed, people are
more likely to talk to the opposite sex (Leskovec and Horvitz
2008). The recent study of Pinterest observed that ladies
tend to be more active and engaged than men (Ottoni et al.
2013), and women and men have different interests (Chang
et al. 2014). We have compared Tumblr’s patterns with other
social networks in Table 1 and observed that most of those
trend hold in Tumblr except for some number difference.
Lampe et al. (Lampe, Ellison, and Steinfield 2007) did a
set of survey studies on Facebook users, and shown that peo-
ple use Facebook to maintain existing offline connections.
Java et al. (Java et al. 2007) presented one of the earli-
est research paper for Twitter, and found that users leverage
Twitter to talk their daily activities and to seek or share infor-
mation. In addition, Schwartz (Gilbert et al. 2013) is one of
the early studies on Pinterest, and from a statistical point of
view that female users repin more but with fewer followers
than male users. While Hochman and Raz (Hochman and
Schwartz 2012) published an early paper using Instagram
data, and indicated differences in local color usage, cultural
production rate, for the analysis of location-based visual in-
formation flows.
Existing studies on user influence are based on social net-
works or content analysis. McGlohon et al. (McGlohon et
al. 2007) found topology features can help us distinguish
blogs, the temporal activity of blogs is very non-uniform
and bursty, but it is self-similar. Bakshy et al. (Bakshy et
al. 2011) investigated the attributes and relative influence
based on Twitter follower graph, and concluded that word-
of-mouth diffusion can only be harnessed reliably by target-
ing large numbers of potential influencers, thereby capturing
average effects. Hopcroft et al. (Hopcroft, Lou, and Tang
2011) studied the Twitter user influence based on two-way
reciprocal relationship prediction. Weng et al. (Weng et al.
2010) extended PageRank algorithm to measure the influ-
ence of Twitter users, and took both the topical similarity
between users and link structure into account. Kwak et al.
(Kwak et al. 2010) study the topological and geographical
properties on the entire Twittersphere and they observe some
notable properties of Twitter, such as a non-power-law fol-
lower distribution, a short effective diameter, and low reci-
procity, marking a deviation from known characteristics of
human social networks.
However, due to data access limitation, majority of the ex-
isting scholar papers are based on either Twitter data or tra-
ditional blogging data. This work closes the gap by provid-
ing the first overview of Tumblr so that others can leverage
as a stepstone to investigate more over this evolving social
service or compare with other related services.
Conclusions and Future Work
In this paper, we provide a statistical overview of Tumblr
in terms of social network structure, content generation and
information propagation. We show that Tumblr serves as a
social network, a blogosphere and social media simultane-
ously. It provides high quality content with rich multime-
dia information, which offers unique characteristics to at-
tract youngsters. Meanwhile, we also summarize and offer
as rigorous comparison as possible with other social services
based on numbers reported in other papers. Below we high-
light some key findings:
• With multimedia support in Tumblr, photos and text ac-
count for majority of blog posts, while audios and videos
are still rare.
• Tumblr, though initially proposed for blogging, yields a
significantly different network structure from traditional
blogosphere. Tumblr’s network is much denser and bet-
ter connected. Close to 29.03% of connections on Tumblr
are reciprocate, while blogosphere has only 3%. The aver-
age distance between two users in Tumblr is 4.7, which is
roughly half of that in blogosphere. The giant connected
component covers 99.61% of nodes as compared to 75%
in blogosphere.
• Tumblr network is highly similar to Twitter and Face-
book, with power-law distribution for in-degree distribu-
tion, non-power law out-degree distribution, positive de-
gree associativity for reciprocate connections, small dis-
tance between connected nodes, and a dominant giant
connected component.
• Without post length limitation, Tumblr users tend to post
longer. Approximately 1/4 of text posts have authentic
10. contents beyond 140 bytes, implying a substantial portion
of high quality blog posts for other tasks like topic
• Those social celebrities tend to be more active. They post
analysis and text mining. and reblog more frequently,
serving as both content generators and information trans-
mitters. Moreover, frequent bloggers like to write short,
while infrequent bloggers spend more effort in writing
longer posts.
• In terms of duration since registration, those long-term
users and recently registered users post less frequently.
Yet, long-term users reblog more.
• Majority of reblog cascades are tiny in terms of both size
and depth, though extreme ones are not uncommon. It
is relatively easier to propagate a message wide but shal-
low rather than deep, suggesting the priority for influence
maximization or information propagation.
• Compared with Twitter, Tumblr is more vibrant and faster
in terms of reblog and interactions. Tumblr reblog has
a strong bias toward recency. Approximately 3/4 of the
first reblogs occur within the first hour and 95.84% appear
within one day.
This snapshot research is by no means to be complete.
There are several directions to extend this work. First, some
patterns described here are correlations. They do not il-
lustrate the underlying mechanism. It is imperative to dif-
ferentiate correlation and causality (Anagnostopoulos, Ku-
mar, and Mahdian 2008) so that we can better understand
the user behavior. Secondly, it is observed that Tumblr is
very popular among young users, as half of Tumblr’s visitor
base being under 25 years old. Why is it so? We need to
combine content analysis, social network analysis, together
with user profiles to figure out. In addition, since more than
70% of Tumblr posts are images, it is necessary to go be-
yond photo captions, and analyze image content together
with other meta information.
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