TY - GEN A1 - Bin Tareaf, Raad A1 - Berger, Philipp A1 - Hennig, Patrick A1 - Meinel, Christoph T1 - Personality exploration system for online social networks BT - Facebook brands as a use case T2 - 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI) N2 - User-generated content on social media platforms is a rich source of latent information about individual variables. Crawling and analyzing this content provides a new approach for enterprises to personalize services and put forward product recommendations. In the past few years, brands made a gradual appearance on social media platforms for advertisement, customers support and public relation purposes and by now it became a necessity throughout all branches. This online identity can be represented as a brand personality that reflects how a brand is perceived by its customers. We exploited recent research in text analysis and personality detection to build an automatic brand personality prediction model on top of the (Five-Factor Model) and (Linguistic Inquiry and Word Count) features extracted from publicly available benchmarks. The proposed model reported significant accuracy in predicting specific personality traits form brands. For evaluating our prediction results on actual brands, we crawled the Facebook API for 100k posts from the most valuable brands' pages in the USA and we visualize exemplars of comparison results and present suggestions for future directions. KW - Big Five Model KW - Brand Personality KW - Personality Prediction KW - Machine Learning KW - Social Media Analysis Y1 - 2019 SN - 978-1-5386-7325-6 U6 - https://doi.org/10.1109/WI.2018.00-76 SP - 301 EP - 309 PB - IEEE CY - New York ER - TY - THES A1 - Bin Tareaf, Raad T1 - Social media based personality prediction models T1 - Social Media-basierte Persönlichkeitsvorhersage Modelle N2 - Individuals have an intrinsic need to express themselves to other humans within a given community by sharing their experiences, thoughts, actions, and opinions. As a means, they mostly prefer to use modern online social media platforms such as Twitter, Facebook, personal blogs, and Reddit. Users of these social networks interact by drafting their own statuses updates, publishing photos, and giving likes leaving a considerable amount of data behind them to be analyzed. Researchers recently started exploring the shared social media data to understand online users better and predict their Big five personality traits: agreeableness, conscientiousness, extraversion, neuroticism, and openness to experience. This thesis intends to investigate the possible relationship between users’ Big five personality traits and the published information on their social media profiles. Facebook public data such as linguistic status updates, meta-data of likes objects, profile pictures, emotions, or reactions records were adopted to address the proposed research questions. Several machine learning predictions models were constructed with various experiments to utilize the engineered features correlated with the Big 5 Personality traits. The final predictive performances improved the prediction accuracy compared to state-of-the-art approaches, and the models were evaluated based on established benchmarks in the domain. The research experiments were implemented while ethical and privacy points were concerned. Furthermore, the research aims to raise awareness about privacy between social media users and show what third parties can reveal about users’ private traits from what they share and act on different social networking platforms. In the second part of the thesis, the variation in personality development is studied within a cross-platform environment such as Facebook and Twitter platforms. The constructed personality profiles in these social platforms are compared to evaluate the effect of the used platforms on one user’s personality development. Likewise, personality continuity and stability analysis are performed using two social media platforms samples. The implemented experiments are based on ten-year longitudinal samples aiming to understand users’ long-term personality development and further unlock the potential of cooperation between psychologists and data scientists. N2 - Menschen haben das Bedürfnis, sich anderen Menschen innerhalb einer bestimmten Gemeinschaft mitzuteilen, indem sie ihre Erfahrungen, Gedanken, Handlungen und Meinungen teilen. Zu diesem Zweck nutzen sie am liebsten moderne Online-Plattformen für soziale Medien wie Twitter, Facebook, persönliche Blogs und Reddit. Die Nutzer dieser sozialen Netzwerke interagieren, indem sie ihre eigenen Status-Updates verfassen, Fotos veröffentlichen und Likes vergeben und dabei eine beträchtliche Menge an Daten hinterlassen, die analysiert werden können. Forscher haben vor kurzem damit begonnen, die in den sozialen Medien geteilten Daten zu untersuchen, um die Online-Nutzer besser zu verstehen und ihre Big-Five-Persönlichkeitseigenschaften vorherzusagen: Verträglichkeit, Gewissenhaftigkeit, Extraversion, Neurotizismus und Offenheit für Erfahrungen. In dieser Arbeit soll der mögliche Zusammenhang zwischen den Big Five Persönlichkeitsmerkmalen der Nutzer und den in ihren Social-Media-Profilen veröffentlichten Informationen untersucht werden. Öffentliche Facebook-Daten wie sprachliche Status-Updates, Metadaten von Likes, Profilbilder, Emotionen oder Reaktionsaufzeichnungen wurden zur Beantwortung der vorgeschlagenen Forschungsfragen herangezogen. Es wurden mehrere Modelle des maschinellen Lernens mit verschiedenen Experimenten erstellt, um die entwickelten Merkmale zu nutzen, die mit den Big 5 Persönlichkeitsmerkmalen korrelieren. Die endgültigen Vorhersageleistungen verbesserten die Vorhersagegenauigkeit im Vergleich zu modernsten Ansätzen, und die Modelle wurden auf der Grundlage etablierter Benchmarks in diesem Bereich bewertet. Die Forschungsexperimente wurden unter Berücksichtigung ethischer Aspekte und des Datenschutzes durchgeführt. Darüber hinaus zielt die Forschung darauf ab, das Bewusstsein für die Privatsphäre von Nutzern sozialer Medien zu schärfen und zu zeigen, was Dritte über die privaten Eigenschaften von Nutzern aus dem, was sie auf verschiedenen sozialen Netzwerkplattformen teilen und tun, herausfinden können. Im zweiten Teil der Arbeit werden die Unterschiede in der Persönlichkeitsentwicklung in einer plattformübergreifenden Umgebung wie Facebook und Twitter untersucht. Die konstruierten Persönlichkeitsprofile in diesen sozialen Plattformen werden verglichen, um die Auswirkungen der verwendeten Plattformen auf die Persönlichkeitsentwicklung eines Nutzers zu bewerten. Ebenso werden Persönlichkeitskontinuität und -stabilität anhand von zwei Social Media Plattformen untersucht. Die durchgeführten Experimente basieren auf zehnjährigen Längsschnittstichproben mit dem Ziel, die langfristige Persönlichkeitsentwicklung der Nutzer zu verstehen und das Potenzial der Zusammenarbeit zwischen Psychologen und Datenwissenschaftlern weiter zu erschließen. KW - social media KW - online personality KW - social networking KW - Online-Persönlichkeit KW - sozialen Medien KW - soziales Netzwerk Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-549142 ER - TY - GEN A1 - Bin Tareaf, Raad A1 - Berger, Philipp A1 - Hennig, Patrick A1 - Meinel, Christoph T1 - ASEDS BT - Towards automatic social emotion detection system using facebook reactions T2 - IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)) N2 - The Massive adoption of social media has provided new ways for individuals to express their opinion and emotion online. In 2016, Facebook introduced a new reactions feature that allows users to express their psychological emotions regarding published contents using so-called Facebook reactions. In this paper, a framework for predicting the distribution of Facebook post reactions is presented. For this purpose, we collected an enormous amount of Facebook posts associated with their reactions labels using the proposed scalable Facebook crawler. The training process utilizes 3 million labeled posts for more than 64,000 unique Facebook pages from diverse categories. The evaluation on standard benchmarks using the proposed features shows promising results compared to previous research. The final model is able to predict the reaction distribution on Facebook posts with a recall score of 0.90 for "Joy" emotion. KW - Emotion Mining KW - Psychological Emotions KW - Machine Learning KW - Social Media Analysis KW - Natural Language Processing Y1 - 2018 SN - 978-1-5386-6614-2 U6 - https://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00143 SP - 860 EP - 866 PB - IEEE CY - New York ER - TY - GEN A1 - Alhosseini Almodarresi Yasin, Seyed Ali A1 - Bin Tareaf, Raad A1 - Najafi, Pejman A1 - Meinel, Christoph T1 - Detect me if you can BT - Spam Bot Detection Using Inductive Representation Learning T2 - Companion Proceedings of The 2019 World Wide Web Conference N2 - Spam Bots have become a threat to online social networks with their malicious behavior, posting misinformation messages and influencing online platforms to fulfill their motives. As spam bots have become more advanced over time, creating algorithms to identify bots remains an open challenge. Learning low-dimensional embeddings for nodes in graph structured data has proven to be useful in various domains. In this paper, we propose a model based on graph convolutional neural networks (GCNN) for spam bot detection. Our hypothesis is that to better detect spam bots, in addition to defining a features set, the social graph must also be taken into consideration. GCNNs are able to leverage both the features of a node and aggregate the features of a node’s neighborhood. We compare our approach, with two methods that work solely on a features set and on the structure of the graph. To our knowledge, this work is the first attempt of using graph convolutional neural networks in spam bot detection. KW - Social Media Analysis KW - Bot Detection KW - Graph Embedding KW - Graph Convolutional Neural Networks Y1 - 2019 SN - 978-1-4503-6675-5 U6 - https://doi.org/10.1145/3308560.3316504 SP - 148 EP - 153 PB - Association for Computing Machinery CY - New York ER - TY - JOUR A1 - Bin Tareaf, Raad A1 - Berger, Philipp A1 - Hennig, Patrick A1 - Meinel, Christoph T1 - Cross-platform personality exploration system for online social networks BT - Facebook vs. Twitter JF - Web intelligence N2 - Social networking sites (SNS) are a rich source of latent information about individual characteristics. Crawling and analyzing this content provides a new approach for enterprises to personalize services and put forward product recommendations. In the past few years, commercial brands made a gradual appearance on social media platforms for advertisement, customers support and public relation purposes and by now it became a necessity throughout all branches. This online identity can be represented as a brand personality that reflects how a brand is perceived by its customers. We exploited recent research in text analysis and personality detection to build an automatic brand personality prediction model on top of the (Five-Factor Model) and (Linguistic Inquiry and Word Count) features extracted from publicly available benchmarks. Predictive evaluation on brands' accounts reveals that Facebook platform provides a slight advantage over Twitter platform in offering more self-disclosure for users' to express their emotions especially their demographic and psychological traits. Results also confirm the wider perspective that the same social media account carry a quite similar and comparable personality scores over different social media platforms. For evaluating our prediction results on actual brands' accounts, we crawled the Facebook API and Twitter API respectively for 100k posts from the most valuable brands' pages in the USA and we visualize exemplars of comparison results and present suggestions for future directions. KW - Big Five model KW - personality prediction KW - brand personality KW - machine KW - learning KW - social media analysis Y1 - 2020 U6 - https://doi.org/10.3233/WEB-200427 SN - 2405-6456 SN - 2405-6464 VL - 18 IS - 1 SP - 35 EP - 51 PB - IOS Press CY - Amsterdam ER -