A Systematic Investigation of Blocking Strategies for Real-time Classification of Social Media Content into Events

Reuter T, Cimiano P. A Systematic Investigation of Blocking Strategies for Real-time Classification of Social Media Content into Events. In: Proceedings of the 6th International Conference on Weblogs and Social Media (ICWSM) - Workshop on Real-Time Analysis and Mining of Social Streams (RAMSS). Palo Alto, California: AAAI Press; 2012.Events play a prominent role in our lives, such that many social media documents describe or are related to some event. Organizing social media documents with respect to events thus seems a promising approach to better manage and organize the ever-increasing amount of user-generated content in social media applications. It would support the navigation of data by events or allow one to get notified about new postings related to the events one is interested in, just to name two applications. A challenge is to automatize this process so that incoming documents can be assigned to their corresponding event without any user intervention. We present a system that is able to classify a stream of social media data into a growing and evolving set of events. In order to scale up to the data sizes and data rates in social media applications, the use of a candidate retrieval or blocking step is crucial to reduce the number of events that are considered as potential candidates to which the incoming data point could belong to. In this paper we present and experimentally compare different blocking strategies along their cost vs. effectiveness tradeoff. We show that using a blocking strategy that selects the 60 closest events with respect to upload time, we reach FMeasures of about 85.1% while being able to process the incoming documents within 32ms on average. We thus provide a principled approach supporting to scale up classification of social media documents into events and to process the incoming stream of documents in real time

Event-based stream classification framework – a supervised clustering approach for social media applications

Reuter T. Event-based stream classification framework – a supervised clustering approach for social media applications. Bielefeld: Universitätsbibliothek; 2015.Events play a very prominent role in our lifes. Therefore many social media documents describe or are related to some event. However, it is difficult for a human to gather relevant information without any structure in these documents. The organization of social media documents with respect to events thus seems to be a promising approach to better manage and organize the ever-increasing amount of content that is shared using social media applications. It is a challenge to automatize this process so that incoming documents can be assigned to their corresponding event without any user intervention. In this dissertation we present an event-based stream classification framework that is able to classify a never-ending stream of social media data into a growing and evolving set of events. By doing this, we successfully perform the assignment of a social media item newly uploaded to some social media site to its corresponding event (if it already exists) or create a new event to which future data items can be assigned. We refer to this problem as the event detection problem and propose to use machine learning techniques to tackle it. We successfully address several key challenges that arise in this context: i) handling the data in a stream-based setting, i.e. addressing the challenges arising from the need to process a never-ending stream of data, ii) scaling to the data sizes and rates usually encountered in social media applications, and iii) tackling the new event detection problem, i.e. the problem of determining whether an incoming data item belongs to a new or to an already known event. We address these challenges through a classification approach allowing us to process the data in one single pass. Furthermore, we include a suitable candidate event retrieval step which retrieves a set of event candidates that the incoming data point is likely to belong to and we include a function trained using machine learning techniques that determines whether the incoming data point belongs to the top-scored candidate or rather to a new event. The performance of our system is maximized using different optimization strategies so that it outperforms many other state-of-the-art approaches. Further, we extend our framework so that it can be used in a multi-pass setting. Using this approach we show that we can improve the quality of the clustering significantly in comparison to the single-pass approach, while also lowering the computational time by one order of magnitude. We show that this extension can be used in a stream-based setting while reaching the quality of a computationally very expensive offline clustering algorithm. We prove that our highly efficient approach is capable of successfully clustering a real-world and non-toy dataset by introducing a new dataset consisting of user-contributed images together with associated metadata describing the events they depict. The dataset was already published earlier and is well known in the community. Our single-pass and multi-pass strategies reach an F-measure score of 88.6% and 93.9%, respectively. In conclusion, we show that our framework is not only capable of addressing the above mentioned challenging issues but also outperforms other state-of-the-art approaches in terms of quality and scalability

ReSEED: Social Event dEtection Dataset

Reuter T, Papadopoulos S, Mezaris V, Cimiano P. ReSEED: Social Event dEtection Dataset. In: MMSys '14. Proceedings of the 5th ACM Multimedia Systems Conference . New York: ACM; 2014: 35-40.Nowadays, digital cameras are very popular among people and quite every mobile phone has a build-in camera. Social events have a prominent role in people’s life. Thus, people take pictures of events they take part in and more and more of them upload these to well-known online photo community sites like Flickr. The number of pictures uploaded to these sites is still proliferating and there is a great interest in automatizing the process of event clustering so that every incoming (picture) document can be assigned to the corresponding event without the need of human interaction. These social events are defined as events that are planned by people, attended by people and for which the social multimedia are also captured by people. There is an urgent need to develop algorithms which are capable of grouping media by the social events they depict or are related to. In order to train, test, and evaluate such algorithms and frameworks, we present a dataset that consists of about 430,000 photos from Flickr together with the underlying ground truth consisting of about 21,000 social events. All the photos are accompanied by their textual metadata. The ground truth for the event groupings has been derived from event calendars on the Web that have been created collaboratively by people. The dataset has been used in the Social Event Detection (SED) task that was part of the MediaEval Benchmark for Multimedia Evaluation 2013. This task required participants to discover social events and organize the related media items in event-specific clusters within a collection of Web multimedia documents. In this paper we describe how the dataset has been collected and the creation of the ground truth together with a proposed evaluation methodology and a brief description of the corresponding task challenge as applied in the context of the Social Event Detection task

Diagnostic based modeling for determining absolute atomic oxygen densities in atmospheric pressure helium-oxygen plasmas

Absolute atomic oxygen ground state densities in a radio-frequency driven atmospheric pressure plasma jet, operated in a helium-oxygen mixture, are determined using diagnostic based modeling. One-dimensional numerical simulations of the electron dynamics are combined with time integrated optical emission spectroscopy. The population dynamics of the upper O 3p 3P (l=844 nm) atomic oxygen state is governed by direct electron impact excitation, dissociative excitation, radiation losses, and collisional induced quenching. Absolute values for atomic oxygen densities are obtained through comparison with the upper Ar 2p1 (l=750.4 nm) state. Results for spatial profiles and power variations are presented and show excellent quantitative agreement with independent two-photon laser-induced fluorescence measurements

Förderung des Beschreibens von prozessorientierter Diagnostik im naturwissenschaftlichen Sachunterricht – Evaluation eines auf Text- und Videovignetten basierenden Seminars für Grundschullehramtsstudierende

Die Fähigkeit, diagnostische Maßnahmen einer Lehrperson in verschiedenen Unterrichtssituationen beschreiben zu können, ist ein Teilprozess der professionellen Wahrnehmung von prozessorientierter Diagnostik. Diese Fähigkeit kann als eine zentrale Voraussetzung für das Umsetzen eigener prozessorientierter Diagnostik im naturwissenschaftlichen Sachunterricht betrachtet werden. Ein vignettenbasiertes Seminar für Grundschullehramtsstudierende vermochte das Beschreiben von diagnostischen Maßnahmen nur teilweise zu fördern. Der Beitrag berichtet die Ergebnisse der Seminarevaluation, diskutiert eine denkbare Weiterentwicklung des Seminars und wirft methodische Fragen zum Erhebungsinstrument auf

The impact of a construction play on 5- to 6-year-old children’s reasoning about stability.

Theory: Young children have an understanding of basic science concepts such as stability, yet their theoretical assumptions are often not concerned with stability. The literature on theory theory and theory-evidence coordination suggests that children construct intuitive theories about their environment which can be adjusted in the face of counterevidence that cannot be assimilated into the prior theory. With increasing age, children acquire a Center theory when balancing objects and try to balance every object at their middle, succeeding with symmetrical objects. Later, they acquire the basic science concept of stability through learning that the weight distribution of an object is of importance. Thus, they acquire a Mass theory and succeed in balancing asymmetrical objects as well. Fluid and crystallized intelligence might contribute to children’s acquisition of Mass theory. Moreover, their Mass theory might be supported by implementing a playful intervention including (a) material scaffolds and (b) verbal scaffolds. Aims: We investigated which theories children have about stability and whether these theories can be adjusted to Mass theory by implementing a playful intervention. Method: A total of 183 5- to 6-year-old children took part in the study with a pre-post-follow-up intervention design. Children’s Mass theory was assessed with an interview in which children explained constructions’ stabilities. The children received a playful intervention with two differing degrees of scaffolding (material scaffolds or material + verbal scaffolds) or no scaffolding. Results: At first few children used a Mass theory to explain their reasoning. However, after being confronted with counterevidence for the asymmetrical constructions, children changed their explanation and applied a Mass theory. More children in the play group with the highest degree of scaffolding, i.e., material + verbal scaffolds, acquired a Mass theory compared to the other groups. Fluid as well as crystallized intelligence contributed to children’s acquisition of a Mass theory. Discussion: Counterevidence can support children in their acquisition of a Mass theory. A playful intervention with scaffolding supports children even more

Social Event Detection at MediaEval: a three-year retrospect of tasks and results

Petkos G, Papadopoulos S, Mezaris V, et al. Social Event Detection at MediaEval: a three-year retrospect of tasks and results. In: Proc. ACM ICMR 2014 Workshop on Social Events in Web Multimedia (SEWM). 2014.This paper presents an overview of the Social Event Detection (SED) task that has been running as part of the MediaEval benchmarking activity for three consecutive years (2011 - 2013). The task has focused on various aspects of social event detection and retrieval and has attracted a significant number of participants. We discuss the evolution of the task and the datasets, we summarize the set of approaches ursued by participants and evaluate the overall collective progress that has been achieved