Hiba Abou Jamra
ABSTRACT
Social networks are becoming increasingly a source of wealth for people to connect with others in the society and express themselves. These networks store huge amounts of data related to individual and collective behavior, and relationships. Despite their importance, there exists few research that explains the factors leading to the evolution of these relationships, as well as abrupt changes in the behavior of individuals in contact. This paper proposes an approach based on the topology of social networks to detect early warnings of such changes, called weak signals. Our approach is in contrast to existing works that focus on analyzing major themes and trends, i.e. strong signals, prevalent in a social network at a particular point in time. We rely on a temporal interaction graph, and extract patterns that characterize weak signals. We demonstrate our approach and validate the detected signals through the analysis of social interactions between individuals of a captive Guinea baboons group, and confirm the existence of weak signals prior to the occurrence of an aggressive behavior.
- Jamey L Ackley, Tejas G Puranik, and Dimitri Mavris. 2020. A supervised learning approach for safety event precursor identification in commercial aviation. In AIAA Aviation 2020 Forum. AIAA, Virtual, 2880.Google Scholar
- Jeanne Altmann. 1974. Observational study of behavior: sampling methods. Behaviour 49, 3-4 (1974), 227–266.Google ScholarCross Ref
- Harry Igor Ansoff. 1975. Managing surprise and discontinuity: strategic response to weak signals. European Institute for Advanced Studies in Management, California.Google Scholar
- H Igor Ansoff and Edward J McDonnell. 1990. Implanting strategic management.Google Scholar
- Vincent Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. 2008. Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment 2008, 10(2008), P10008.Google Scholar
- Brian Coffman. 1997. Weak signal research, part I: Introduction. Journal of Transition Management 2, 1 (1997), 4.Google Scholar
- Toby Davies and Elio Marchione. 2015. Event networks and the identification of crime pattern motifs. PloS one 10, 11 (2015), e0143638.Google ScholarCross Ref
- Valeria Gelardi, Jeanne Godard, Dany Paleressompoulle, Nicolas Claidière, and Alain Barrat. 2020. Measuring social networks in primates: wearable sensors versus direct observations. Proceedings of the Royal Society A 476, 2236 (2020), 20190737.Google ScholarCross Ref
- Michel Godet. 1994. From anticipation to action: a handbook of strategic prospective. UNESCO publishing, Michigan.Google Scholar
- Khim-Yong Goh, Cheng-Suang Heng, and Zhijie Lin. 2013. Social media brand community and consumer behavior: Quantifying the relative impact of user-and marketer-generated content. Information systems research 24, 1 (2013), 88–107.Google Scholar
- Dina Q Goldin and Paris C Kanellakis. 1995. On similarity queries for time-series data: constraint specification and implementation. In International Conference on Principles and Practice of Constraint Programming. Springer, Berlin, Heidelberg, 137–153.Google ScholarCross Ref
- Elina Hiltunen. 2008. The future sign and its three dimensions. Futures 40, 3 (2008), 247–260.Google Scholar
- Tomaž Hočevar and Janez Demšar. 2014. A combinatorial approach to graphlet counting. Bioinformatics 30, 4 (12 2014), 559–565. https://doi.org/10.1093/bioinformatics/btt717 arXiv:https://academic.oup.com/bioinformatics/article-pdf/30/4/559/17345305/btt717.pdfGoogle Scholar
- Jiajia Huang, Min Peng, Hua Wang, Jinli Cao, Wang Gao, and Xiuzhen Zhang. 2017. A probabilistic method for emerging topic tracking in Microblog stream. World Wide Web 20 (03 2017). https://doi.org/10.1007/s11280-016-0390-4Google ScholarDigital Library
- Hiba Abou Jamra, Marinette Savonnet, and Éric Leclercq. 2021. Detection of Event Precursors in Social Networks: A Graphlet-Based Method. In Research Challenges in Information Science - 15th International Conference, RCIS 2021, May 11-14, 2021, Proceedings(Lecture Notes in Business Information Processing, Vol. 415), Samira Si-Said Cherfi, Anna Perini, and Selmin Nurcan (Eds.). Springer, Limassol, Cyprus, 205–220. https://doi.org/10.1007/978-3-030-75018-3_13Google Scholar
- Hiba Abou Jamra, Marinette Savonnet, and Éric Leclercq. 2022. BEAM: A Network Topology Framework to Detect Weak Signals. International Journal of Advanced Computer Science and Applications 13, 4(2022), 12.Google ScholarCross Ref
- Hyunuk Kim, Sang-Jin Ahn, and Woo-Sung Jung. 2019. Horizon scanning in policy research database with a probabilistic topic model. Technological Forecasting and Social Change 146 (2019), 588–594.Google ScholarCross Ref
- Hayoung Kim, Yoonsun Han, Juyoung Song, and Tae Min Song. 2019. Application of social big data to identify trends of school bullying forms in South Korea. International journal of environmental research and public health 16, 14(2019), 2596.Google Scholar
- Lee-Nam Kwon, Jun-Hwan Park, Yeong-Ho Moon, Bangrae Lee, YoungHo Shin, and Young-Kuk Kim. 2018. Weak signal detecting of industry convergence using information of products and services of global listed companies-focusing on growth engine industry in South Korea. Journal of Open Innovation: Technology, Market, and Complexity 4, 1(2018), 10.Google ScholarCross Ref
- Xueming Luo, Jie Zhang, and Wenjing Duan. 2013. Social media and firm equity value. Information Systems Research 24, 1 (2013), 146–163.Google ScholarDigital Library
- Julien Maitre, Michel Ménard, Guillaume Chiron, Alain Bouju, and Nicolas Sidère. 2019. A Meaningful Information Extraction System for Interactive Analysis of Documents. In International Conference on Document Analysis and Recognition (ICDAR). IEEE, Sydney, Australia, 92–99. https://doi.org/10.1109/ICDAR.2019.00024Google ScholarCross Ref
- Benjamin A Miller, Michelle S Beard, and Nadya T Bliss. 2011. Eigenspace analysis for threat detection in social networks. In 14th International Conference on Information Fusion. IEEE, USA, 1–7.Google Scholar
- Yue Ning, Sathappan Muthiah, Huzefa Rangwala, and Naren Ramakrishnan. 2016. Modeling Precursors for Event Forecasting via Nested Multi-Instance Learning. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (San Francisco, California, USA) (KDD ’16). Association for Computing Machinery, New York, NY, USA, 1095–1104. https://doi.org/10.1145/2939672.2939802Google ScholarDigital Library
- Fernando Perez and Brian E Granger. 2015. Project Jupyter: Computational narratives as the engine of collaborative data science. Retrieved September 11, 207 (2015), 108.Google Scholar
- Nataša Pržulj. 2007. Biological network comparison using graphlet degree distribution. Bioinformatics 23, 2 (2007), e177–e183.Google ScholarDigital Library
- Natasa Pržulj, Derek G Corneil, and Igor Jurisica. 2004. Modeling interactome: scale-free or geometric?Bioinformatics 20, 18 (2004), 3508–3515.Google Scholar
- Pedro Ribeiro, Pedro Paredes, Miguel EP Silva, David Aparicio, and Fernando Silva. 2019. A survey on subgraph counting: concepts, algorithms and applications to network motifs and graphlets. arXiv preprint arXiv:1910.13011 54, 2 (2019), pp. 1–36.Google Scholar
- Pauline Rousseau, Daniel Camara, and Dimitris Kotzinos. 2021. Weak signal detection and identification in large data sets: a review of methods and applications.Google Scholar
- Barbara L. van Veen, J. Roland Ortt, and P.G. Badke-Schaub. 2019. Compensating for perceptual filters in weak signal assessments. Futures 108(2019), 1–11. https://doi.org/10.1016/j.futures.2019.02.018Google ScholarCross Ref
- Janghyeok Yoon. 2012. Detecting weak signals for long-term business opportunities using text mining of Web news. Expert Systems with Applications 39, 16 (2012), 12543–12550.Google ScholarDigital Library
- Fattane Zarrinkalam and Ebrahim Bagheri. 2017. Event identification in social networks. Encyclopedia with Semantic Computing and Robotic Intelligence 1, 01(2017), 1630002.Google ScholarCross Ref
Index Terms
- Identification of Weak Signals in a Temporal Graph of Social Interactions
Recommendations
Weak Signals as Predictors of Real-World Phenomena in Social Media
ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015Global and national events in recent years have shown that online social media can be a force for good (e.g., Arab Spring) and harm (e.g., the London riots). In both of these examples, social media played a key role in group formation and organization, ...
Building social capital with Facebook: Type of network, availability of other media, and social self-efficacy matter#
Highlights- Type of friends affects building social capital via Facebook and traditional media.
AbstractFindings about Facebook's effect on relationships are mixed, possibly due to lack of models that acknowledge differences across users, types of their friends, and use of competing media. To address this, we proposed and tested how ...
A media-based social interactions analysis procedure
SAC '12: Proceedings of the 27th Annual ACM Symposium on Applied ComputingThere is a growing number of opportunities for users to share media using Web content providers. Moreover, in the context of Web-based Social Networks, there is also the possibility that shared media will be used in subsequent social interactions. ...
Comments