TY - GEN
T1 - Beyond Words
T2 - 13th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, SBP-BRiMS 2020
AU - Swartz, Melanie
AU - Crooks, Andrew
AU - Croitoru, Arie
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Social media content analysis often focuses on just the words used in documents or by users and often overlooks the structural components of document composition and linguistic style. We propose that document structure and emoji use are also important to consider as they are impacted by individual communication style preferences and social norms associated with user role and intent, topic domain, and dissemination platform. In this paper we introduce and demonstrate a novel methodology to conduct structural content analysis and measure user consistency of document structures and emoji use. Document structure is represented as the order of content types and number of features per document and emoji use is characterized by the attributes, position, order, and repetition of emojis within a document. With these structures we identified user signatures of behavior, clustered users based on consistency of structures utilized, and identified users with similar document structures and emoji use such as those associated with bots, news organizations, and other user types. This research compliments existing text mining and behavior modeling approaches by offering a language agnostic methodology with lower dimensionality than topic modeling, and focuses on three features often overlooked: document structure, emoji use, and consistency of behavior.
AB - Social media content analysis often focuses on just the words used in documents or by users and often overlooks the structural components of document composition and linguistic style. We propose that document structure and emoji use are also important to consider as they are impacted by individual communication style preferences and social norms associated with user role and intent, topic domain, and dissemination platform. In this paper we introduce and demonstrate a novel methodology to conduct structural content analysis and measure user consistency of document structures and emoji use. Document structure is represented as the order of content types and number of features per document and emoji use is characterized by the attributes, position, order, and repetition of emojis within a document. With these structures we identified user signatures of behavior, clustered users based on consistency of structures utilized, and identified users with similar document structures and emoji use such as those associated with bots, news organizations, and other user types. This research compliments existing text mining and behavior modeling approaches by offering a language agnostic methodology with lower dimensionality than topic modeling, and focuses on three features often overlooked: document structure, emoji use, and consistency of behavior.
KW - Data mining
KW - Emojis
KW - Social media
KW - User behavior modeling
UR - https://www.scopus.com/pages/publications/85094139964
U2 - 10.1007/978-3-030-61255-9_1
DO - 10.1007/978-3-030-61255-9_1
M3 - Conference contribution
AN - SCOPUS:85094139964
SN - 9783030612542
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 11
BT - Social, Cultural, and Behavioral Modeling - 13th International Conference, SBP-BRiMS 2020, Proceedings
A2 - Thomson, Robert
A2 - Bisgin, Halil
A2 - Dancy, Christopher
A2 - Hyder, Ayaz
A2 - Hussain, Muhammad
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 18 October 2020 through 21 October 2020
ER -