Abstract
Human communication, either online or offline, is characterized by when information is shared from one actor to the other and by what specific information is exchanged. Using text as a way to represent the exchanged information, we can represent human communication systems with a temporal text network model where actors and messages coexist in a dynamic multilayer network. In this model, actors and messages are represented in separate layers, connected by inter-layer temporal edges representing the communication acts—who and when communicate what information. In this chapter we revisit somemeasures specifically developed for temporal networks, and extend them to the case of temporal text networks. In particular, we focus on defining measures relevant for the analysis of information propagation, including the concepts of walk, path, temporal precedence and path distance measures. We conclude by discussing how to use the proposed measures in practice by conducting a comparative analysis in a sample communication network based on Twitter mentions.
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Notes
- 1.
NLP stands for “Natural Language Processing”.
- 2.
To simplify the notation, in this chapter we are assuming that i ≤ j ⇒ t i ≤ t j.
- 3.
We considered only politicians who were either members of the parliament before the elections or were part of an electoral ballot.
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Acknowledgements
We would like to thank Prof. Christian Rohner for his comments and suggestions.
This work was partially supported by the European Community through the project “Values and ethics in Innovation for Responsible Technology in Europe” (Virt-EU) funded under Horizon 2020 ICT-35-RIA call Enabling Responsible ICT-related Research and Innovation.
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Vega, D., Magnani, M. (2019). Metrics for Temporal Text Networks. In: Holme, P., Saramäki, J. (eds) Temporal Network Theory. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-23495-9_8
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