Based on a 4-Facet-Model (Dunkel et al. 1), objects, bases and relations that exist in various LBSN are assigned to the following five key dimensions.
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.
Objects organized under the Social Facet currently include:
|Origin||A Location Based Social Network consisting of a large group of people|
|CompositeKey||A Composite Key used to reference unique objects across different LBSN|
|User||A single user (e.g. a profile or an account) on a location based social network (LBSN)|
|UserGroup||A single group of users on a LBSN|
|Language||A common language used on LBSN, relating to a larger group of people sharing the same language|
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. 2), or the stance of different actors to events as inferred from semantics such as titles, comments or descriptions etc. (Zeng et al. 3). Keywords such as hashtags, for example, may indicate what aspects of an event were perceived as being of particular importance (Towne et al. 4), or refer to individual event consequences or actions people have undertaken or plan to undertake (Gao et al. 5).
Objects organized under the Topical Facet currently include:
|Post||An single post on a location based social network (LBSN) providing original (new) content|
|PostReaction||A reaction on a location based social network (LBSN) such as like, quote, share etc.|
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.
Objects organized under the Spatial Facet currently include:
|Place||A particular (named) place on a location based social network (LBSN).|
|City||A city on a location based social network (LBSN).|
|Country||A country on a location based social network (LBSN).|
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.
Objects organized under the Temporal Facet currently include:
|Event||An (named) event with a representation on LBSN.|
Alexander Dunkel, Gennady Andrienko, Natalia Andrienko, Dirk Burghardt, Eva Hauthal, and Ross Purves. A conceptual framework for studying collective reactions to events in location-based social media. International Journal of Geographical Information Science, 33(4):780–804, 2018. doi:10.1080/13658816.2018.1546390. ↩
Li Zeng, Kate Starbird, and Emma Spiro. # unconfirmed: classifying rumor stance in crisis-related social media messages. In Proceedings of the International AAAI Conference on Web and Social Media, volume 10. 2016. ↩
W Ben Towne, Carolyn P Rosé, and James D Herbsleb. Measuring similarity similarly: lda and human perception. ACM Transactions on Intelligent Systems and Technology (TIST), 8(1):1–28, 2016. ↩
Huiji Gao, Jalal Mahmud, Jilin Chen, Jeffrey Nichols, and Michelle Zhou. Modeling user attitude toward controversial topics in online social media. In Proceedings of the International AAAI Conference on Web and Social Media, volume 8. 2014. ↩