Estimating the Impact of User Personality Traits on electronic Word-of-Mouth Text-mining Social Media Platforms

Transcription

1 Estimating the Impact of User Personality Traits on electronic Word-of-Mouth Text-mining Social Media Platforms Panos Adamopoulos Goizueta Business School Emory University Anindya Ghose Stern School of Business New York University Vilma Todri Goizueta Business School Emory University

2 Digital Media Usage Source: emarketer, 2015 P. Adamopoulos e-wom: Text-mining Social Media Big Data 2/28

3 WOM, Product Reviews, and Advertisements Source: emarketer, 2015 P. Adamopoulos e-wom: Text-mining Social Media Big Data 3/28

4 Social Media and WOM Social media are changing the way we communicate, collaborate, and consume Marketers move beyond one-way messaging harnessing social connections and e-wom Effectiveness of e-wom might be moderated by various factors How are the personalities of social media users associated with purchases by their peers and online economic behavior? P. Adamopoulos e-wom: Text-mining Social Media Big Data 4/28

5 Research Questions Does the latent personality similarity on social media between the source and recipient of WOM messages affect the economic behavior of the recipient after exposure to WOM messages? Do specific combinations of latent personality characteristics of the sender and recipient of WOM messages on social media affect the economic behavior of the recipient after exposure to WOM messages? P. Adamopoulos e-wom: Text-mining Social Media Big Data 5/28

6 Personality Model Agreeableness Conscientiousness Extraversion Emotional range Openness P. Adamopoulos e-wom: Text-mining Social Media Big Data 6/28

7 Empirical Context P. Adamopoulos e-wom: Text-mining Social Media Big Data 7/28

8 WOM Message Visibility Sender s WOM Message Peer Peer does not *text #hashtag #hashtag *text } ü ü {@mention #hashtag *text #hashtag } ü û WOM message not visible to timeline of the follower P. Adamopoulos e-wom: Text-mining Social Media Big Data 8/28

9 Empirical Data All social commerce transactions that were generated through the aforementioned process on Twitter s social platform Information about product offerings Users specific information and social network information Actual content of messages, etc. P. Adamopoulos e-wom: Text-mining Social Media Big Data 9/28

10 Model Specification (Personality Similarity) User interactions, common friends and followers, topics discussed: (NLP 140 million messages, natural number of topics, etc.) Psycholinguistics, Deep-learning techniques Sentiment analysis, Personalization of message *Similar for personality combinations (RQ2) P. Adamopoulos e-wom: Text-mining Social Media Big Data 10/28

11 Linguistic Analytics (Dictionary-based method) Collection of unstructured data Preprocessing of corpus Match with LIWC Inference of personality P. Adamopoulos e-wom: Text-mining Social Media Big Data 11/28

12 Out-of-Sample Performance (Personality Similarity) P. Adamopoulos e-wom: Text-mining Social Media Big Data 12/28

13 Outline of Results (Personality Similarity) Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 User similarity (Social network) *** *** *** *** *** *** Reciprocal relationship *** *** *** *** *** *** # Peer-to-Peer interactions Sentiment of message *** *** *** *** *** *** Personalized message *** *** *** *** *** *** 47.58% higher User expertise (Sender) *** *** *** *** *** *** likelihood of purchase User leadership (Sender) *** *** *** *** *** ** User similarity (Personality) *** User similarity (Agreeableness) * *** *** *** *** User similarity (Conscientiousness) User similarity (Extraversion) *** User similarity (Emotional range) User similarity (Openness) Visible message= *** ** ** *** Visible message=1 x User similarity (Agreeableness) ** * * ** Visible message=1 x User similarity (Conscientiousness) Visible message=1 x User similarity (Extraversion) ** *** *** *** Visible message=1 x User similarity (Emotional range) Visible message=1 x User similarity (Openness) Log-likelihood χ " P. Adamopoulos e-wom: Text-mining Social Media Big Data 13/28

14 Out-of-Sample Performance (Personality Characteristics) P. Adamopoulos e-wom: Text-mining Social Media Big Data 14/28

15 Outline of Results (Personality Characteristics) Model B1 (Agreeableness) Low level (Sender) x Low level (Recipient) *** Low level (Sender) x High level (Recipient) *** High level (Sender) x Low level (Recipient) ** High level (Sender) x High level (Recipient) Agreeable users are more effective disseminators Additional controls Yes Log-likelihood χ " P. Adamopoulos e-wom: Text-mining Social Media Big Data 15/28

16 Outline of Results (Personality Characteristics) Model B2 (Conscientiousness) Low level (Sender) x Low level (Recipient) * Low level (Sender) x High level (Recipient) High level (Sender) x Low level (Recipient) *** High level (Sender) x High level (Recipient) *** Conscientious users are more effective disseminators Additional controls Yes Log-likelihood χ " P. Adamopoulos e-wom: Text-mining Social Media Big Data 16/28

17 Outline of Results (Personality Characteristics) Model B3 (Extraversion) Low level (Sender) x Low level (Recipient) * Low level (Sender) x High level (Recipient) *** High level (Sender) x Low level (Recipient) *** High level (Sender) x High level (Recipient) Additional controls Yes Log-likelihood χ " Extrovert-to-introvert: 71.28% increase in the likelihood of purchase P. Adamopoulos e-wom: Text-mining Social Media Big Data 17/28

18 Outline of Results (Personality Characteristics) Model B4 (Emotional range) Low level (Sender) x Low level (Recipient) *** Low level (Sender) x High level (Recipient) High level (Sender) x Low level (Recipient) High level (Sender) x High level (Recipient) ** Additional controls Yes Low emotional range to high emotional range: 61.19% increase in the likelihood of purchase Log-likelihood χ " P. Adamopoulos e-wom: Text-mining Social Media Big Data 18/28

19 Outline of Results (Personality Characteristics) Model B5 (Openness) Low level (Sender) x Low level (Recipient) Low level (Sender) x High level (Recipient) High level (Sender) x Low level (Recipient) *** High level (Sender) x High level (Recipient) *** Open users are more effective disseminators Additional controls Yes Log-likelihood χ " P. Adamopoulos e-wom: Text-mining Social Media Big Data 19/28

20 Robustness Checks Discrete choice models Alternative survival model specifications Parametric survival analysis, accelerated failure-time model, etc. Possibly correlated observations Propensity score matching Structural modeling (latent variable model) Deep-learning techniques for personality inference Deep-learning techniques for latent attributes and traits P. Adamopoulos e-wom: Text-mining Social Media Big Data 20/28

21 Linguistic Model with Deep Learning Techniques Words as dense numeric vectors Semantics of words (vs. bag-of-words) distances between words and phrases USA New York Manhattan Brooklyn France Paris Camera Restaurant Book P. Adamopoulos e-wom: Text-mining Social Media Big Data 21/28

22 Linguistic Analytics ( Word embedding method) Preprocessing of unstructured data Numeric representation of words Representation of personality factors Representation of users Inference of personality P. Adamopoulos e-wom: Text-mining Social Media Big Data 22/28

23 Out-of-Sample Performance P. Adamopoulos e-wom: Text-mining Social Media Big Data 23/28

24 Controls for Latent Homophily and Network Roles Continuous feature representations for nodes in graphs Representation reflects homophily and structural equivalence DeepWalk: Online Learning of Social Representations. B. Perozzi, R. Al-Rfou, and S. Skiena. International Conference on Knowledge Discovery and Data Mining (KDD), node2vec: Scalable Feature Learning for Networks. A. Grover, J. Leskovec. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), P. Adamopoulos e-wom: Text-mining Social Media Big Data 24/28

25 Managerial Implications (I) Engineering WOM Increase sales and spur buzz by encouraging or incenting users with specific personality traits to generate or disseminate WOM Brand management Associating brands with certain personality characteristics Fostering perceptions appealing to specific personality types Business value of big data Demonstrate the value of directly observing the WOM instances and extracting knowledge from analyzing granular level data and unstructured user-generated content in social media P. Adamopoulos e-wom: Text-mining Social Media Big Data 25/28

26 Managerial Implications (II) Machine learning for unstructured data Demonstrate to firms the ability to conduct such analyses leveraging machine-learning algorithms Monetization strategies Asymmetric effects across different personality types e.g., pricing for sponsored content based on user traits Algorithmic curation of content Latent personality characteristics of the social media users to curate content more effectively and drive engagement Better predict diffusion P. Adamopoulos e-wom: Text-mining Social Media Big Data 26/28

27 Conclusions and Managerial Implications Latent personality characteristics and similarity affect users online purchase behavior and facilitate e-wom on social media Significant economic importance of effects Managerial implications Social brands: Engineering WOM, brand management, business value of big data, machine learning for unstructured data, etc. Social platforms: Monetization strategies, algorithmic curation of content, etc. P. Adamopoulos e-wom: Text-mining Social Media Big Data 27/28

28 Thank You! P. Adamopoulos e-wom: Text-mining Social Media Big Data