Analysis and Forecasting of Trending Topics in Online Media Streams

Among the vast information available on the web, social media streams capture what people currently pay attention to and how they feel about certain topics. Awareness of such trending topics plays a crucial role in multimedia systems such as trend aware recommendation and automatic vocabulary selection for video concept detection systems. Correctly utilizing trending topics requires a better understanding of their various characteristics in different social media streams. To this end, we present the first comprehensive study across three major online and social media streams, Twitter, Google, and Wikipedia, covering thousands of trending topics during an observation period of an entire year. Our results indicate that depending on one's requirements one does not necessarily have to turn to Twitter for information about current events and that some media streams strongly emphasize content of specific categories. As our second key contribution, we further present a novel approach for the challenging task of forecasting the life cycle of trending topics in the very moment they emerge. Our fully automated approach is based on a nearest neighbor forecasting technique exploiting our assumption that semantically similar topics exhibit similar behavior. We demonstrate on a large-scale dataset of Wikipedia page view statistics that forecasts by the proposed approach are about 9-48k views closer to the actual viewing statistics compared to baseline methods and achieve a mean average percentage error of 45-19% for time periods of up to 14 days.Comment: ACM Multimedia 201

Find Me the Right Content! Diversity-Based Sampling of Social Media Spaces for Topic-Centric Search

Social media and networking websites, such as Twitter and Facebook, generate large quantities of information and have become mechanisms for real-time content dissipation to users. An important question that arises is: how do we sample such social media information spaces in order to deliver relevant content on a topic to end users? Notice that these large-scale information spaces are inherently diverse, featuring a wide array of attributes such as location, recency, degree of diffusion effects in the network and so on. Naturally, for the end user, different levels of diversity in social media content can significantly impact the information consumption experience: low diversity can provide focused content that may be simpler to understand, while high diversity can increase breadth in the exposure to multiple opinions and perspectives. Hence to address our research question, we turn to diversity as a core concept in our proposed sampling methodology. Here we are motivated by ideas in the "compressive sensing" literature and utilize the notion of sparsity in social media information to represent such large spaces via a small number of basis components. Thereafter we use a greedy iterative clustering technique on this transformed space to construct samples matching a desired level of diversity. Based on Twitter Firehose data, we demonstrate quantitatively that our method is robust, and performs better than other baseline techniques over a variety of trending topics. In a user study, we further show that users find samples generated by our method to be more interesting and subjectively engaging compared to techniques inspired by state-of-the-art systems, with improvements in the range of 15--45%

Should We Use the Sample? Analyzing Datasets Sampled from Twitter's Stream API

National Research Foundation (NRF) Singapore under International Research Centre @ Singapore Funding Initiativ

Quantifying & characterizing information diets of social media users

An increasing number of people are relying on online social media platforms like Twitter and Facebook to consume news and information about the world around them. This change has led to a paradigm shift in the way news and information is exchanged in our society – from traditional mass media to online social media. With the changing environment, it’s essential to study the information consumption of social media users and to audit how automated algorithms (like search and recommendation systems) are modifying the information that social media users consume. In this thesis, we fulfill this high-level goal with a two-fold approach. First, we propose the concept of information diets as the composition of information produced or consumed. Next, we quantify the diversity and bias in the information diets that social media users consume via the three main consumption channels on social media platforms: (a) word of mouth channels that users curate for themselves by creating social links, (b) recommendations that platform providers give to the users, and (c) search systems that users use to find interesting information on these platforms. We measure the information diets of social media users along three different dimensions of topics, geographic sources, and political perspectives. Our work is aimed at making social media users aware of the potential biases in their consumed diets, and at encouraging the development of novel mechanisms for mitigating the effects of these biases.Immer mehr Menschen verwenden soziale Medien, z.B. Twitter und Facebook, als Quelle für Nachrichten und Informationen aus ihrem Umfeld. Diese Entwicklung hat zu einem Paradigmenwechsel hinsichtlich der Art undWeise, wie Informationen und Nachrichten in unserer Gesellschaft ausgetauscht werden, geführt – weg von klassischen Massenmedien hin zu internetbasierten Sozialen Medien. Angesichts dieser veränderten (Informations-) Umwelt ist es von entscheidender Bedeutung, den Informationskonsum von Social Media-Nutzern zu untersuchen und zu prüfen, wie automatisierte Algorithmen (z.B. Such- und Empfehlungssysteme) die Informationen verändern, die Social Media- Nutzer aufnehmen. In der vorliegenden Arbeit wird diese Aufgabenstellung wie folgt angegangen: Zunächst wird das Konzept der “Information Diets” eingeführt, das eine Zusammensetzung aus produzierten und konsumierten Social Media-Inhalten darstellt. Als nächstes werden die Vielfalt und die Verzerrung (der sogenannte “Bias”) der “Information Diets” quantifiziert die Social Media-Nutzer über die drei hauptsächlichen Social Media- Kanäle konsumieren: (a) persönliche Empfehlungen und Auswahlen, die die Nutzer manuell pflegen und wodurch sie soziale Verbindungen (social links) erzeugen, (b) Empfehlungen, die dem Nutzer von der Social Media-Plattform bereitgestellt werden und (c) Suchsysteme der Plattform, die die Nutzer für ihren Informationsbedarf verwenden. Die “Information Diets” der Social Media-Nutzer werden hierbei anhand der drei Dimensionen Themen, geographische Lage und politische Ansichten gemessen. Diese Arbeit zielt zum einen darauf ab, Social Media-Nutzer auf die möglichen Verzerrungen in ihrer “Information Diet” aufmerksam zu machen. Des Weiteren soll diese Arbeit auch dazu anregen, neuartige Mechanismen und Algorithmen zu entwickeln, um solche Verzerrungen abzuschwächen