TY - JOUR A1 - Heusinger, Moritz T1 - Dimensionality reduction in the context of dynamic social media data streams JF - Evolving Systems N2 - In recent years social media became an important part of everyday life for many people. A big challenge of social media is, to find posts, that are interesting for the user. Many social networks like Twitter handle this problem with so-called hashtags. A user can label his own Tweet (post) with a hashtag, while other users can search for posts containing a specified hashtag. But what about finding posts which are not labeled by the creator? We provide a way of completing hashtags for unlabeled posts using classification on a novel real-world Twitter data stream. New posts will be created every second, thus this context fits perfectly for non-stationary data analysis. Our goal is to show, how labels (hashtags) of social media posts can be predicted by stream classifiers. In particular, we employ random projection (RP) as a preprocessing step in calculating streaming models. Also, we provide a novel real-world data set for streaming analysis called NSDQ with a comprehensive data description. We show that this dataset is a real challenge for state-of-the-art stream classifiers. While RP has been widely used and evaluated in stationary data analysis scenarios, non-stationary environments are not well analyzed. In this paper, we provide a use case of RP on real-world streaming data, especially on NSDQ dataset. We discuss why RP can be used in this scenario and how it can handle stream-specific situations like concept drift. We also provide experiments with RP on streaming data, using state-of-the-art stream classifiers like adaptive random forest and concept drift detectors. Additionally, we experimentally evaluate an online principal component analysis (PCA) approach in the same fashion as we do for RP. To obtain higher dimensional synthetic streams, we use random Fourier features (RFF) in an online manner which allows us, to increase the number of dimensions of low dimensional streams. KW - social media KW - data Y1 - 2021 UR - https://doi.org/10.1007/s12530-021-09396-z SN - 1868-6486 ER - TY - JOUR A1 - Klemm, Marcus T1 - Well-being Changes from Year to Year BT - A Comparison of Current, Remembered and Predicted Life Satisfaction JF - Journal of Happiness Studies N2 - I study yearly changes in personal well-being combining data on current, retrospective and prospective life satisfaction from the German Socio-Economic Panel. Predicted and remembered changes in life satisfaction are both positive on average and match well, whereas the average year to year-change inferred from reports of current life satisfaction is negative. Retrospective assessments of past well-being are strongly influenced by current life satisfaction, significantly related to past life satisfaction and linked to past predictions of current satisfaction. Due to different problems related to the ordinal measurement scale, changes in subjective reference systems and recall ability, the analysis overall suggests that direct reports of intertemporal changes provide valuable additional information for the analysis of individual well-being. KW - well-being KW - life Y1 - 2021 UR - https://doi.org/10.1007/s10902-021-00468-0 SN - 1573-7780 ER -