TY  - CHAP
A1  - Bensberg, Frank
A1  - Auth, Gunnar
A1  - Czarnecki, Christian
A1  - Wörndle, Christopher
ED  - Kemal İlter, H.
T1  - Transforming literature-intensive research processes through text analytics – design, implementation and lessons learned
N2  - The continuing growth of scientific publications raises the question how research processes can be digitalized and thus realized more productively. Especially in information technology fields, research practice is characterized by a rapidly growing volume of publications. For the search process various information systems exist. However, the analysis of the published content is still a highly manual task. Therefore, we propose a text analytics system that allows a fully digitalized analysis of literature sources. We have realized a prototype by using EBSCO Discovery Service in combination with IBM Watson Explorer and demonstrated the results in real-life research projects. Potential addressees are research institutions, consulting firms, and decision-makers in politics and business practice.
KW  - Text analytics
KW  - Text mining
KW  - Literature review
KW  - Research process
Y1  - 2018
U6  - http://dx.doi.org/10.6084/m9.figshare.7582073.v1
N1  - 5th International Management Information Systems Conference.
October 24-26 2018, Ankara
ER  - 
TY  - CHAP
A1  - Büsgen, André
A1  - Klöser, Lars
A1  - Kohl, Philipp
A1  - Schmidts, Oliver
A1  - Kraft, Bodo
A1  - Zündorf, Albert
ED  - Cuzzocrea, Alfredo
ED  - Gusikhin, Oleg
ED  - Hammoudi, Slimane
ED  - Quix, Christoph
T1  - From cracked accounts to fake IDs: user profiling on German telegram black market channels
T2  - Data Management Technologies and Applications
N2  - Messenger apps like WhatsApp and Telegram are frequently used for everyday communication, but they can also be utilized as a platform for illegal activity. Telegram allows public groups with up to 200.000 participants. Criminals use these public groups for trading illegal commodities and services, which becomes a concern for law enforcement agencies, who manually monitor suspicious activity in these chat rooms. This research demonstrates how natural language processing (NLP) can assist in analyzing these chat rooms, providing an explorative overview of the domain and facilitating purposeful analyses of user behavior. We provide a publicly available corpus of annotated text messages with entities and relations from four self-proclaimed black market chat rooms. Our pipeline approach aggregates the extracted product attributes from user messages to profiles and uses these with their sold products as features for clustering. The extracted structured information is the foundation for further data exploration, such as identifying the top vendors or fine-granular price analyses. Our evaluation shows that pretrained word vectors perform better for unsupervised clustering than state-of-the-art transformer models, while the latter is still superior for sequence labeling.
KW  - Clustering
KW  - Natural language processing
KW  - Information extraction
KW  - Profile extraction
KW  - Text mining
Y1  - 2023
SN  - 978-3-031-37889-8 (Print)
SN  - 978-3-031-37890-4 (Online)
U6  - http://dx.doi.org/10.1007/978-3-031-37890-4_9
N1  - 10th International Conference, DATA 2021, Virtual Event, July 6–8, 2021, and 11th International Conference, DATA 2022, Lisbon, Portugal, July 11-13, 2022
SP  - 176
EP  - 202
PB  - Springer
CY  - Cham
ER  -