http://dl.gi.de/handle/20.500.12116/11089 Thu, 23 Jul 2026 07:01:25 GMT 2026-07-23T07:01:25Z http://dl.gi.de/handle/20.500.12116/11332 Learning to Discover Political Activism in the Twitterverse Finn, Samantha; Mustafaraj, Eni When analysing social media conversations, in search of the public opinion about an unfolding political event that is being discussed in real-time (e.g., presidential debates, major speeches, etc.), it is important to distinguish between two groups of participants: political activists and the general public. To address this problem, we propose a supervised machine-learning approach, which uses inexpensively acquired labeled data from mono-thematic Twitter accounts to learn a binary classifier for the labels “political activist” and “general public”. While the classifier has a 92 % accuracy on individual tweets, when applied to the last 200 tweets from accounts of a set of 1000 Twitter users, it classifies accounts with a 97 % accuracy. Our work demonstrates that machine learning algorithms can play a critical role in improving the quality of social media analytics and understanding, whose importance is increasing as social media adoption becomes widespread. Tue, 01 Jan 2013 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/11332 2013-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11330 A Brief Tutorial on How to Extract Information from User-Generated Content (UGC) Egger, Marc; Lang, André In this brief tutorial, we provide an overview of investigating text-based user-generated content for information that is relevant in the corporate context. We structure the overall process along three stages: collection, analysis, and visualization. Corresponding to the stages we outline challenges and basic techniques to extract information of different levels of granularity. Tue, 01 Jan 2013 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/11330 2013-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11328 News Tue, 01 Jan 2013 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/11328 2013-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/11338 From Texts to Networks: Detecting and Managing the Impact of Methodological Choices for Extracting Network Data from Text Data Diesner, Jana This thesis (Diesner in Technical Report CMU-ISR-12-101, 2012) addresses a series of methodological problems related to extracting information on socio-technical networks from natural language text data. Theories and models from the social sciences are leveraged and combined with computational approaches to (a) construct, analyze and compare network data and (b) combine text data and network data for analysis. This thesis entails various projects that serve three purposes: First, the impact of various common coding choices, including reference resolution and co-occurrence-based link formation, on network data and analysis results is empirically identified across multiple types of text data and domains. Second, different relation extraction methods are compared across various over-time, open-source, large-scale datasets with respect to the resulting network data and analysis results. This study offers a complement to traditional strategies for accuracy assessment. The relation extraction methods considered include network data construction based on (a) manually versus automatically built thesauri, (b) meta-data, and (c) collaboration with subject matter experts. Third, the concepts of grouping and roles from network analysis are integrated with text mining methods to enable the theoretically grounded, joint consideration of text data and network data for real-world applications.Overall, in this thesis, an interdisciplinary and computationally rigorous approach is used; thereby advancing the intersection of network analysis, natural language processing and computing. The contributions made with this work help people to utilize text data for network analysis, and to collect, manage and interpret rich network data at any scale. These steps are preconditions for asking substantive and graph-theoretic questions, testing hypotheses, and advancing theories about networks. Tue, 01 Jan 2013 00:00:00 GMT http://dl.gi.de/handle/20.500.12116/11338 2013-01-01T00:00:00Z