Hierarchy Identification in Social Networks: Literature ... · V. Zayats and M. Ostendorf, “Conversation modeling on reddit using a graph-structured lstm,” arXiv preprint arXiv:1704.02080,

Hierarchy Identification in Social Networks:

Literature Review

Denys [email protected]

2Title: Hierarchy Identification in Social Networks - Literature ReviewAuthor: Denys Katerenchuk Email: [email protected]

MotivationHello

Hello

Hello


MotivationHello

Hello

Hello


Motivation


Applications● National Security

● Advertisement

● E-commerce

● News, Politics


Overview1. Background

a. Motivationb. Historyc. Datad. Evaluation

2. Methodsa. Structure Analysisb. Language Analysis

i. N-gramii. Hedgingiii. Entrainmentiv. Politeness

c. Neural Network

3. Conclusion


History

380 BC


History

(Jacob Moreno 1932)


History

Stirman, Shannon Wiltsey, and James W. Pennebaker. "Word use in the poetry of suicidal and nonsuicidal poets." Psychosomatic medicine 63.4 (2001): 517-522.


Data

Shetty, Jitesh, and Jafar Adibi. "The Enron email dataset database schema and brief statistical report." Information sciences institute technical report, University of Southern California 4.1 (2004): 120-128.

www.wikipedia.org www.reddit.com www.stackexchange.com


Data

www.twitter.com1. Viswanath, Bimal, et al. "On the evolution of user interaction in facebook." Proceedings of the 2nd ACM workshop on Online social networks. ACM, 2009.

2. Wilson, Christo, et al. "Beyond social graphs: User interactions in online social networks and their implications." ACM Transactions on the Web (TWEB) 6.4 (2012): 17.


EvaluationAccuracy F-measure

where p and r are precision and recall


EvaluationAverage Precision Mean Average Precision

where P(k) = precision@k and ∆R(k) = |recall(k − 1) − recall(k)|.

where Q = a set of ordering problems and q = a single evaluation instance


EvaluationKendall's tau Discounted Cumulative

Gain

where c - a number of concordant (i.e. a correct relative ranking) pairs and d - a number ofdiscordant (i.e. an incorrect relative ranking) pairs.

where n - a number of elements and rel() - some relevance function of the i-th element in a given list.

where IDCG - represents the ideal DCG


Evaluation


Overview1. Background

a. Motivationb. Historyc. Datad. Evaluation

2. Methodsa. Structure Analysisb. Language Analysis

i. N-gramii. Hedgingiii. Entrainmentiv. Politeness

c. Neural Network

3. Conclusion


Structure Analysis


Structure AnalysisDegree Centrality


Structure AnalysisCloseness Centrality


Structure AnalysisBetweenness Centrality


Structure AnalysisEigenvector Centrality


Structure Analysis

J. Diesner and K. Carley, “Exploration of communication networks from the enron email corpus,” SIAM International Conference on Data Mining

● Closeness Centrality● Eigenvector Centrality

Structure Analysis Structure Analysisv.s.

A. Agarwal, A. Omuya, A. Harnly, and O. Rambow, “A comprehensive gold standardfor the enron organizational hierarchy,” in Proceedings of the 50th Annual Meeting ofthe Association for Computational Linguistics

E. Gilbert, “Phrases that signal workplace hierarchy,” in Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work, CSCW ’12


Structure Analysis

J. Diesner and K. Carley, “Exploration of communication networks from the enron email corpus,” SIAM International Conference on Data Mining

● Closeness Centrality● Eigenvector Centrality

Structure Analysis Structure Analysisv.s.

A. Agarwal, A. Omuya, A. Harnly, and O. Rambow, “A comprehensive gold standardfor the enron organizational hierarchy,” in Proceedings of the 50th Annual Meeting ofthe Association for Computational Linguistics


J. W. Johnsen and K. Franke, “Feasibility study of social network analysis on loosely structured communication networks,” Procedia Computer Science, vol. 108, pp. 2388– 2392, 2017


Language Analysis

N-gram model SVM


Language AnalysisWordNet

C. Fellbaum, “A semantic network of english verbs,” WordNet: An electronic lexical database, vol. 3, pp. 153–178, 1998

LIWC

J. W. Pennebaker, R. L. Boyd, K. Jordan, and K. Blackburn, “The development andpsychometric properties of liwc2015,”


Language Analysis

P. Bramsen, M. Escobar-Molano, A. Patel, and R. Alonso, “Extracting social power relationships from natural language,” in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pp. 773–782, Association for Computational Linguistics, 2011.



Hedges"Whenever you have time,

let's give it a try!"

G. Lakoff, “Hedges: A study in meaning criteria and the logic of fuzzy concepts,” Journal of philosophical logic, vol. 2, no. 4, pp. 458–508, 1973.

E. Choi, C. Tan, L. Lee, C. Danescu-Niculescu-Mizil, and J. Spindel, “Hedge detection as a lens on framing in the gmo debates: A position paper,” in Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics, pp. 70–79, Association for Computational Linguistics, 2012.


Entrainment

M. E. Ireland, R. B. Slatcher, P. W. Eastwick, L. E. Scissors, E. J. Finkel, and J. W.Pennebaker, “Language style matching predicts relationship initiation and stability,”Psychological science, vol. 22, no. 1, pp. 39–44, 2011.


Entrainment

C. Danescu-Niculescu-Mizil, M. Gamon, and S. Dumais, “Mark my words!: linguistic style accommodation in social media,” in Proceedings of the 20th international conference on World wide web, pp. 745–754, ACM, 2011.

C. Danescu-Niculescu-Mizil, L. Lee, B. Pang, and J. Kleinberg, “Echoes of power: Language effects and power differences in social interaction,” in Proceedings of the 21st international conference on World Wide Web, pp. 699–708, ACM, 2012.


Politeness

C. Danescu-Niculescu-Mizil, M. Sudhof, D. Jurafsky, J. Leskovec, and C. Potts, “A computational approach to politeness with application to social factors,” arXiv preprint arXiv:1306.6078, 2013


NN: Word Embeddings

G. E. Hinton, J. L. McClelland, and D. E. Rumelhart, “Parallel distributed processing: Explorations in the microstructure of cognition, vol. 1,” ch. Distributed Representations, pp. 77–109, Cambridge, MA, USA: MIT Press, 1986.

Y. Bengio, R. Ducharme, P. Vincent, and C. Jauvin, “A neural probabilistic language model,” Journal of machine learning research, vol. 3, no. Feb, pp. 1137–1155, 2003.


NN: Word Embeddings

T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” arXiv preprint arXiv:1301.3781, 2013.

T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, “Distributed representations of words and phrases and their compositionality,” in Advances in neural information processing systems, pp. 3111–3119, 2013.


NN: Hedge

Adel, Heike, and Hinrich Schütze. "Exploring different dimensions of attention for uncertainty detection." arXiv preprint arXiv:1612.06549 (2016).


NN: Hedge

Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.


NN: Hedge

Adel, Heike, and Hinrich Schütze. "Exploring different dimensions of attention for uncertainty detection." arXiv preprint arXiv:1612.06549 (2016).


NN: LSTM

Gers, Felix A., Jürgen Schmidhuber, and Fred Cummins. "Learning to forget: Continual prediction with LSTM." (1999): 850-855.


NN: Graph-structured LSTM

V. Zayats and M. Ostendorf, “Conversation modeling on reddit using a graph-structured lstm,” arXiv preprint arXiv:1704.02080, 2017.

Hierarchy Identification in Social Networks: Literature Review

Denys [email protected]

Summary:

•Hierarchy detection is a hard problem•Structure and Language are indicative•Neural Networks will make to improveresults in this area

Documents

Hierarchy Identification in Social Networks: Literature ... · V. Zayats and M. Ostendorf, “Conversation modeling on reddit using a graph-structured lstm,” arXiv preprint arXiv:1704.02080,