Skip to content

Facets

Based on a 4-Facet-Model (Dunkel et al. 2018 1), objects, bases and relations that exist in various LBSN are assigned to the following five key dimensions.

Social Facet

Who?

The social facet describes a specific user identity, but also encompassing wider affiliation with social groups or cultures. The underlying assumption is that events, to which people react to on Social Media, involve or affect different groups of people differently. In other words, whether someone feels affected or unaffected, is considered a participant, observer or witness, or takes a positive or negative stance, depends, to some degree, on the social background of this individual. This may encompass complex aspects including political orientation, beliefs, values, norms and preferences, which express a continuum of people’s relationships towards an event that (often unconsciously) affect reactions.

List of objects organized under the Social Facet.

Topical Facet

What?

The difference between the thematic and social facet is in the relation to the user. Thematic attributes include immediate situational aspects that affect reactions from an in a particular situation (e.g. sentiments, feelings, emotions, or any other attributes of the reaction environment). Therefore, unlike social attributes, thematic attributes change frequently from one reaction to another. Possible questions explored in visual analytics include but are not limited to emotional states of the actor (e.g. positive, neutral, negative), as inferred from emoticons or based on sentiment analysis (Bai et al. 2019 2), or the stance of different actors to events as inferred from semantics such as titles, comments or descriptions etc. (Zeng et al. 2016 3). Keywords such as hashtags, for example, may indicate what aspects of an event were perceived as being of particular importance (Towne et al. 2016 4), or refer to individual event consequences or actions people have undertaken or plan to undertake (Gao et al. 2014 5).

Topical or Thematic?

You may see the terms topical and thematic used interchangeably in various parts of this documentation and corresponding publications. We are not picky about these details - there are valid reasons for using either one of these terms.

The topical facet may encompass many different aspects and you may even use another more specific term that fits better to your context of use.

For instance, other terms used in literature include "State-oriented context" (Etzion and Niblett 2010 6), "Interest topic" (Zimmermann et al. 2007 7), or "Activity/Status" (Baldauf et al. 2007 8).

List of objects organized under the Topical Facet.

Spatial Facet

Where?

Both time and space are referenced from many objects in LBSN, such as the location of a post, the date when a user joins a Social Network or when a place is added to a map. Beyond these references, independent spatial objects of LBSN Networks are frequently found at different levels of granularity. Spatial information found on LBSN is of high relevancy to user privacy, because most location data can be used to track or identify users. In the base LBSN Structure we consider objects on four levels of spatial granularity, Coordinate, Place, City and Country, which can be further subdivided based on personal needs.

List of objects organized under the Spatial Facet.

Temporal Facet

When?

Both time and space are referenced from many objects in LBSN such as the time of sharing a post, the date when a user joins a Social Network or when someone reacts to something. Beyond these references, an explicit temporal object frequently found on LBSN is the Event. Similar to objects of the spatial facet, events may cover a range of granularities crossing different hierarchical levels.

List of objects organized under the Temporal Facet.

Relationships

Interlinkage and relations between objects in LBSN can be considered as an additional, fifth facet.

See the specific section on Relationships.

  1. Dunkel, A., Andrienko, G., Andrienko, N., Burghardt, D., Hauthal, E., & Purves, R. (2018). A conceptual framework for studying collective reactions to events in location-based social media. International Journal of Geographical Information Science, 33(4), 780--804. https://doi.org/10.1080/13658816.2018.1546390 ::: ↩

  2. Bai, Q., Dan, Q., Mu, Z., & Yang, M. (2019). A systematic review of emoji: Current research and future perspectives. Frontiers in Psychology, 10, 2221. https://doi.org/10.3389/fpsyg.2019.02221 ::: ↩

  3. Zeng, L., Starbird, K., & Spiro, E. (2016).\ # unconfirmed: Classifying rumor stance in crisis-related social media messages. Proceedings of the International AAAI Conference on Web and Social Media, 10. ::: ↩

  4. Towne, W. B., Rosé, C. P., & Herbsleb, J. D. (2016). Measuring similarity similarly: Lda and human perception. ACM Transactions on Intelligent Systems and Technology (TIST), 8(1), 1--28. ::: ↩

  5. Gao, H., Mahmud, J., Chen, J., Nichols, J., & Zhou, M. (2014). Modeling user attitude toward controversial topics in online social media. Proceedings of the International AAAI Conference on Web and Social Media, 8. ::: ↩

  6. Etzion, O., & Niblett, P. (2010). Event processing in action (p. 325). Manning Publications. http://dl.acm.org/citation.cfm?id=1894960 ::: ↩

  7. Zimmermann, A., Lorenz, A., & Oppermann, R. (2007). An operational definition of context. Lecture Notes in Computer Science, 4635, 558--571. https://doi.org/10.1007/978-3-540-74255-5\_42 ::: ↩

  8. Baldauf, M., Dustdar, S., & Rosenberg, F. (2007). A survey on context-aware systems. Information Systems, 2(4). https://doi.org/10.1504/IJAHUC.2007.014070 ::: ↩

Last update: March 1, 2022
Back to top