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Methods and Techniques for Segmentation of Consumers in Social Media

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Social media has revolutionised the way in which consumers relate to each other and with brands. The opinions published in social media have a power of influencing purchase decisions as important as advertising campaigns. Consequently, marketers are increasing efforts and investments for obtaining indicators to measure brand health from the digital content generated by consumers.
Given the unstructured nature of social media contents, the technology used for processing such contents often implements Artificial Intelligence techniques, such as natural language processing, machine learning and semantic analysis algorithms.
This thesis contributes to the State of the Art, with a model for structuring and integrating the information posted on social media, and a number of techniques whose objectives are the identification of consumers, as well as their socio-demographic and psychographic segmentation. The consumer identification technique is based on the fingerprint of the devices they use to surf the Web and is tolerant to the changes that occur frequently in such fingerprint. The psychographic profiling techniques described infer the position of consumer in the purchase funnel, and allow to classify the opinions based on a series of marketing attributes. Finally, the socio-demographic profiling
techniques allow to obtain the residence and gender of consumers.

Published in: Data & Analytics
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Methods and Techniques for Segmentation of Consumers in Social Media

  1. 1. Methods and Techniques for Segmentation of Consumers in Social Media Ph.D. Thesis Óscar Muñoz García Advisors: Prof. Dr. Asunción Gómez Pérez Dr. Raúl García Castro December 2nd 2015
  2. 2. Table of Contents 2 • Introduction • State of the Art • Approach • Social Media Ontology for Consumer Analytics • Morphosyntactic Characterisation of Social Media Contents • Technique for Unique User Identification • Techniques for Consumers Segmentation • Conclusions and Future Work
  3. 3. Marketing paradigm shift from a traditional communication model… 3 • Marketing is the process of communicating the value of a product to consumers for the purpose of selling it to them - Goal: understand the most adequate way to reach consumers • Investment in mass media (TV, Radio, Press, Outdoor, etc.) • Communication performance is measured through KPIs based on audience (GRPs) • Consumer insights are obtained through opinion surveys Introduction Brand Brand Brand Brand Brand Brand Brand Brand Brand Brand Brand Brand Brand Brand
  4. 4. … to a digitalised & social communication model 4 • Consumers are the most influent media (producing content in social media) • Communication performance is measured through assorted KPIs (visibility, engagement, conversion, recommendation, …) • Market insights are extended with the analysis of content published in social media by consumers Introduction 70% social media users take into account opinions published by others 65% search info about product and brands 53% express positive comments on brands 50% express complaints at least once per month 92% trust word-of-mouth above other forms of advertising [Nielsen, 2012] 1BN+ people connected through social and mobile platforms
  5. 5. Challenges 5 Introduction • Small tabular data • Segmentation of consumer groups • Understand consumer through audience measurement and opinion surveys • Big Data - Integration of multiple heterogeneous & unstructured data sources at scale • Segmentation of individual consumers - Unique user identification • Understand consumers through content analysis - Insights beyond volume and polarity GOAL: to provide techniques for extracting consumer segmentations from the content generated by consumers in social media, their profile metadata, and their activities when navigating social media websites
  6. 6. Table of Contents 6 • Introduction • State of the Art - Vocabularies for Representing Social Media Information - Techniques for Tracking Users in the Web - Technique for Detecting the Evolution of Temporary Records - Marketing Background • Approach • Social Media Ontology for Consumer Analytics • Morphosyntactic Characterisation of Social Media Contents • Technique for Unique User Identification • Techniques for Consumers Segmentation • Conclusions and Future Work
  7. 7. Vocabularies for Representing Social Media Information 7 • Social media silos - Lack of semantic connections among the different channels • Users, contents, topics, etc. - Multiple data formats and data models • Facebook Graph API, Google API, Twitter API, RSS, Atom, etc. • Existing vocabularies State of the Art SIOC Cross-social network integration FOAF Users and Relations Dublin Core Multimedia Resources SKOS Conceptual Schemas WGS84 Geo-localisation Time Zone Time Zones and Political Regions Schema.org Information extraction ISOCat Linguistic Information Marl Opinion Polarity Onyx Annotation of Emotions Wordnet-Affect Affective Categories RDFG Named-graph annotation Open Research Problems • Lack of data models for representing social media information for the marketing domain • Lack of characterisations of social media according to linguistic features of content
  8. 8. Techniques for Tracking Users in the Web 8 • Capturing web activity - Based on web logs - Based on web beacons - Based on JavaScript tags - Based on packet sniffing • Identifying unique users - Based on cookies - Based on fingerprint State of the Art 4 5 Site Analytics Services 1 2 3 Website 1 servers 2 3 Website 2 servers Data Collector 83.6% of browsers have a unique fingerprint 94.2% of browsers with JVM or Flash installed have a unique fingerprint If a browser is taken at random at most 1 in 286,777 browsers share the same fingerprint 37.4% fingerprints changed during the period of study (very unstable) [Eckersley, 2010] Open Research Problem • The fingerprint-based technique fails when devices’ fingerprints evolve over time
  9. 9. Technique for Detecting the Evolution of Temporary Records 9 • Determines if two records define the same entity or different entities • Takes into account the time elapsed between the capture of the records • Defines two probabilities: State of the Art Disagreement decay: probability that an entity changes the value of an attribute A within Δt d¹ (A,Dt) Agreement decay: probability that two different entities share the same value of the value of A within Δt d= (A,Dt) • Provides a technique for learning the agreement and disagreement decays • Describes algorithms for clustering records that correspond to the same entity [Li et al., 2011]
  10. 10. Marketing Background 10 • The Consumer Decision Journey (a.k.a. Purchase Funnel) - Different process models with main stages in common [Lewis, 1903; Forrester, 2010; McKinsey, 2009] State of the Art Awareness Evaluation Purchase Post-purchase Experience • The Marketing Mix - [Borden 1964; McCarthy and Brogowicz; 1981] - The marketing elements to take into account to make business operations more profitable Product Place Pric e Promotion Quality Point of Sale Price Promotion Design Customer Service Sponsorship Advertisement • Research on human Emotions - [Plutchik, 1989; Ekman, 2005; Shaver et al., 1987] Category Polarity + - SD satisfaction dissatisfaction TF trust fear HS happiness sadness LH love hate • Extraction of socio-demographic profiles (Location and gender) - From declared information - From panels, social media profile metadata
  11. 11. Marketing Background 11 Open Research Problems • Lack of techniques for the classification of opinions according to psychographic attributes used in the marketing domain for consumer segmentation 1. For classifying opinions according to the Consumer Decision Journey framework 2. For identifying Marketing Mix attributes in opinions 3. For identifying emotions in Spanish that go beyond polarity • The existing techniques for socio-demographic segmentation can be improved 1. Coverage and accuracy for identifying locations can be improved by combining different kinds of metadata (e.g., user descriptions, friendship networks, locations found in content) 2. The existing techniques for identifying gender do not take advantage of linguistic information that can be extracted from the content (e.g., gender concord) State of the Art
  12. 12. Table of Contents 12 • Introduction • State of the Art • Approach - Research Methodology - Objectives - Contributions to the State of the Art - Assumptions - Hypotheses - Restrictions • Social Media Ontology for Consumer Analytics • Morphosyntactic Characterisation of Social Media Contents • Technique for Unique User Identification • Techniques for Consumers Segmentation • Conclusions and Future Work
  13. 13. Research Methodology 13 Approach 1. Explorative Research - Define the research problem and the hypothesis to be tested 2. Experimental Research - Propose a solution based on the hypotheses to fulfil research objectives and design experiments to validate the hypotheses Explorative Research Experimental Research Review of the State of the Art Define Problem, Hypotheses, and Objectives Propose Solution Design Experiments & Evaluate
  14. 14. Objective 1 14 To provide a data model for structuring social media information that is useful for marketing purposes Contribution: C1. Social media ontology for consumer analytics Assumptions: A1. It is possible to structure the content and metadata published on social media according to a single normalised data schema A2. The information structured according to this data model can be used for higher- level Business Intelligence processes Approach
  15. 15. Objective 2 15 To characterise social media channels from the point of view of the morphosyntactic characteristics of their textual contents Contribution: C2. Morphosyntactic characterisation of social media contents Hypothesis: H1. Social media contents statistically present different morphosyntactic features depending on the specific kind of media where they have been published Restriction: R1. TreeTagger for Spanish used; PoS distributions may vary with other PoS tagger Approach
  16. 16. Objective 3 16 To provide a fingerprint-based technique for identifying unique users that is tolerant to changes in the device fingerprint Contribution: C3. Technique for unique user identification based on evolving fingerprint detection Hypothesis: H2. Online activity can be grouped and identified effectively through the digital fingerprint, even when fingerprint varies over time Restrictions: R1. Cross-device user identification is out of the scope R9. Deployment and scalability validation is out of the scope Approach
  17. 17. Objective 4 17 To provide a collection of automatic techniques for extracting consumer segmentations from the analysis of user-generates content Contribution: C4. Techniques for segmentation of consumers from social media content Assumption: A3. Consumers' demographic and psychographic profiles can be obtained, even if those profiles are not declared explicitly by the user, by analysing their contents and metadata Restrictions: R2. Only textual content R3. Psychographic profiles: Consumer Decision Journey, Marketing Mix and Emotions R4. Socio-demographic profiles: Place of Residence and Gender R8. Corpora used: automotive, banking, beverages, sports, telecommunications, food, retail and utilities R9. Deployment and scalability validation is out of the scope R10. Freeling for executing the lemmatisation, PoS tagging, and dependency parsing Approach
  18. 18. Objectives 4.1, 4.2 and 4.3 18 To provide techniques for classifying consumer opinions according to the Consumer Decision Journey, the Marketing Mix and the Emotions frameworks Contributions: C4.1, C4.2 and C4.3. Techniques for detecting Consumer Decision Journey stages, Marketing Mix attributes and Emotions within user-generated content Hypotheses: H3, H4 and H5. Consumers utilise different expressions for referring to these categories. If we are able to identify the particular linguistic expressions used, we will be able to classify texts into the categories and, therefore, segment consumers accordingly Restrictions: R5 and R6. Consumer Decision Journey and Marketing Mix - English and Spanish. R7. Emotions - Spanish Approach
  19. 19. Objectives 4.4 and 4.5 19 To provide techniques for recognising the place of residence and gender of social media users that improve the accuracy of existing techniques Contribution: C4.4 and C4.5. Techniques for detecting the place of residence and gender of social media users Hypothesis: H6. Social networks’ homophily [McPherson et al., 2001] can be used for improving the accuracy of existing techniques H7. The linguistic concord existing in the posts written in Spanish that explicitly mention social media users can be exploited for enhancing the coverage Approach
  20. 20. Table of Contents 20 • Introduction • State of the Art • Approach • Social Media Ontology for Consumer Analytics • Morphosyntactic Characterisation of Social Media Contents • Technique for Unique User Identification • Techniques for Consumers Segmentation • Conclusions and Future Work
  21. 21. Social Graph Ontology 21 Social Media Ontology for Consumer Analytics dcterms sgo tzont isocat skos rdfg sioc foaf onyx marl geo schema Ontology Network Ontology Modules Vocabularies Classes Properties Instances Reused 11 26 85 17 New 1 5 74 22 Total 12 31 159 39 Method Followed • NeOn methodology for building ontology networks [Suárez-Figueroa et al., 2012] • Implementation of the guidelines for reusing ontological resources
  22. 22. Table of Contents 22 • Introduction • State of the Art • Approach • Social Media Ontology for Consumer Analytics • Morphosyntactic Characterisation of Social Media Contents • Technique for Unique User Identification • Techniques for Consumers Segmentation • Conclusions and Future Work
  23. 23. Experiment Settings 23 Morphosyntactic Characterisation of Social Media Content • Random sample of 10,000 posts written in Spanish uniformly distributed among the following media types - Blogs - Forums - Microblogs - Social Networks - Review Sites - Audio-visual content - News • PoS categories analysed - Main categories: noun, adjective, adverb, determiner, conjunction, pronoun, preposition, punctuation mark, verb - Secondary categories: proper nouns, quantity adjective, negation adverb, coordinating conjunction, personal pronoun, comma, lexical verb, etc.
  24. 24. Distribution of PoS categories 24 Morphosyntactic Characterisation of Social Media Content The distribution of every PoS category varies across different social media types Hypothesis Validation H1. The contents published in social media statistically present different morphosyntactic features depending on the specific kind of media where they have been published News Blogs Video Reviews Microblogs Forums Social networks Nouns 31% 30% 29% 23% 34% 22% 33% Adjectives 9% 8% 6% 8% 9% 7% 6% Adverbs 2% 3% 3% 5% 4% 4% 3% Determiners 11% 10% 8% 8% 6% 8% 7% Conjunctions 6% 8% 7% 10% 6% 10% 7% Pronouns 2% 3% 5% 6% 5% 6% 4% Personal Pronouns 33% 38% 49% 56% 59% 68% 55% Prepositions 15% 15% 12% 13% 8% 12% 11% Punctuaction marks 11% 8% 13% 9% 8% 9% 11% Verbs 12% 14% 17% 18% 19% 21% 16%
  25. 25. Table of Contents 25 • Introduction • State of the Art • Approach • Social Media Ontology for Consumer Analytics • Morphosyntactic Characterisation of Social Media Contents • Technique for Unique User Identification • Techniques for Consumers Segmentation • Conclusions and Future Work
  26. 26. Technique Description 26 Technique for Unique User Identification based on Evolving Fingerprint Detection • Clustering algorithm for determining fingerprints that correspond to a single device • Four variants for assigning weights to attributes used for calculating fingerprint similarity 1. Uniform weights 2. Based on learned attribute entropy 1. Based on learned time decay 2. Hybrid (combination of attribute entropy and time decay) Disagreement decay of the Plugins attribute Agreement decay of the Plugins attribute X X-Forwarded-For X-Real-IP Plugins Fonts User-Agent Video Accept-Language Time Zone Accept Accept-Charset Accept-Encoding IE Persistence Cache-Control Session Storage H(X) 12,52 12,5 11,76 8,38 7,51 5,5 3,68 2,3 2,05 1,89 1,81 0,56 0,29 0,29
  27. 27. Evaluation 27 Technique for Unique User Identification based on Evolving Fingerprint Detection • The four variants have been evaluated being the hybrid one based on decay an entropy the most accurate • Our technique outperforms the previous approach by Eckersley [2010] - Improvements on Accuracy and False Positive Rate • Coverage (% of browsers classified) - Baseline (Eckersley) = 65% • Only those with Java and Flash installed - Results = 100% Measure Value Baseline (Eckersley) Rand index (accuracy) 0.9998 0.991 Error rate 0.0001 n.a. Recall 0.87 n.a. Specificity 0.99993 n.a. False positive rate 0.00007 0.0086 False negative rate 0.13 n.a. Precision 0.93 n.a. F-measure 0.9 n.a. Purity 0.94 n.a. Hypothesis Validation H2. The online activity generated by consumers in social media can be grouped and identified effectively through the digital fingerprint of their devices, even when such fingerprint varies over time
  28. 28. Table of Contents 28 • Introduction • State of the Art • Social Media Ontology for Consumer Analytics • Morphosyntactic Characterisation of Social Media Contents • Technique for Unique User Identification • Techniques for Consumers Segmentation - Detection of Consumer Decision Journey Stages - Detection of Marketing Mix Attributes - Detection of Emotions - Detection of Place of Residence - Detection of Gender • Conclusions and Future Work
  29. 29. Method Followed 29 Techniques for Segmentation of Consumers from Social Media Content • CRISP-DM [Shearer, 2000] (Cross Industry Standard Process for Data Mining) Modeling Data Business Understanding Data Understanding Data Preparation Evaluation Deployment 1. Collect Initial Data • Search contents • Download and extract • Opinion Clipping 2. Describe Data 3. Explore Data 4. Verify Data Quality 1. Select and Clean Data • Filter irrelevant content • Language detection • Filter SPAM • Manual revision 2. Construct Data • Micropost normalisation
  30. 30. Detection of Consumer Decision Journey, Marketing Mix and Emotions 30 Techniques for Segmentation of Consumers from Social Media Content Consumer Decision Journey Marketing Mix Emotions Classification Categories Awareness Evaluation Purchase Post-Purchase Quality Design Customer Service Point of Sale Promotion Price Sponsorship Advertising Trust-Fear Satisfaction-Dissatisfaction Happiness-Sadness Love-Hate Corpus Size 13,980 English 22,731 Spanish 13,980 English 22,731 Spanish 26,505 Spanish AMT Annotator Agreement (Kappa) 0,503 (moderate) 0,397 (fair) 0,415 (moderate) Modelling Technique Classification rules engineered by UPF Decision trees learned with C4.5 algorithm Classification rules engineered by UPM
  31. 31. Rule Based Modelling Technique 31 Techniques for Segmentation of Consumers from Social Media Content • Classification rules - <Linguistic Pattern>  <Classification Action> - Linguistic patterns • Sequences of words, lemmas, PoS categories (or combinations), wildcards and entities - Classification actions • Arithmetic operations for assigning a numerical value to a given category • Chunk rules - Linguistic patterns that delimit the scope of classification rules “,” [CC] . Al principio no me convenció , pero tras probarlo me quedé con el producto [PP1] quedar#V_IS1 “con” * _ENTITY_ -> POSTPURCHASE + 1 … finalmente me quedé con el iPad … ser [RG] odioso#A -> LH - 2 … la experiencia de usuario es bastante odiosa …
  32. 32. Evaluation of Detection of Consumer Decision Journey Stages 32 Techniques for Segmentation of Consumers from Social Media Content Evaluation Results for English Evaluation Results for Spanish Baseline Results in line with existing approaches for the identification of wishes - Precision between 56% and 86.7% Hypothesis Validation H3. Consumers utilise different expressions during their decision journey. If we are able to identify the particular linguistic expressions used in each of the stages of the purchase process, we will be able to classify texts along the different phases
  33. 33. Evaluation of Detection of Marketing Mix Attributes 33 Techniques for Segmentation of Consumers from Social Media Content Evaluation Results for English Evaluation Results for Spanish Baseline There are no previous attempts for detecting marketing mix attributes Hypothesis Validation H4. The vocabulary used by consumers can be used to identify the Marketing Mix attributes they are referring to. If we are able to identify the particular lexical elements that refer to such attributes, we will be able to classify text according to the Marketing Mix framework
  34. 34. Evaluation of Detection of Emotions 34 Techniques for Segmentation of Consumers from Social Media Content Hypothesis Validation H5. Consumers utilise different expressions to express their sentiment about brands beyond their pleasure and displeasure. If we are able to identify the linguistic expressions used for each of these sentiments, we will be able to classify texts along the different emotions Market tool Our work Precision 0.85 0.84 Recall 0.21 0.58 Baseline (polarity identification)
  35. 35. Detection of Place of Residence 35 Techniques for Segmentation of Consumers from Social Media Content 1. Declared Location 2. Friendship Network 3. Profile Description 4. Locations in posts 5. Hybrid (friendship) (if not result)
  36. 36. Evaluation of Detection of Place of Residence 36 Techniques for Segmentation of Consumers from Social Media Content • Dataset - 1,080 users whose city of residence is known - Distributed among 11 different countries - Publish content in English and Spanish Approach Accuracy 1. Declared location 0.81 2. Friendship Network 0.86 3. Profile Description 0.81 4. Locations in Posts 0.81 5. Hybrid 0.81 Baseline Precision of previous approaches from 51% to 71% Hypothesis Validation H6. Social networks’ homophily can be used for improving the accuracy of existing techniques for identifying the place of residence
  37. 37. Detection of Gender 37 Techniques for Segmentation of Consumers from Social Media Content 1. Profile Name 2. Gender Concord (if not result)
  38. 38. 0,97% 0,98% 0,78% 0,8% Female% Male% Precision) User%Names% Men2ons%to%Users% 0,87% 0,8% 0,95% 0,85% Female% Male% Recall& User%Names% Men3ons%to%Users% 0,97% 0,98% 0,78% 0,8% Female% Male% Precision) User%Names% Men2ons%to%Users% Evaluation of Detection of Place of Residence 38 Techniques for Segmentation of Consumers from Social Media Content • Dataset - Evaluation of coverage: 69,261 users (including names and tweets) - Evaluation of precision and recall: 1,509 users annotated with gender • Coverage (% of users classified) - Baseline (using names) = 66% - Results (using gender concord) = 67% (gain of 1%) - Good results taking into account that 22% users have no gender (e.g., organisations) • Precision and Recall: Hypothesis Validation H7. The linguistic concord existing in the posts written in Spanish that explicitly mention social media users can be exploited for enhancing the coverage over the techniques that only make use of the name declared by users in their profiles
  39. 39. Table of Contents 39 • Introduction • State of the Art • Approach • Social Media Ontology for Consumer Analytics • Morphosyntactic Characterisation of Social Media Contents • Technique for Unique User Identification • Techniques for Consumers Segmentation • Conclusions and Future Work
  40. 40. Conclusions and Future Work 40 Conclusions and Future Work C1. Social media ontology for consumer analytics - First attempt to a holistic user model from the marketing perspective - Can be used by marketing professionals for enriching CRM and DMP applications C2. Morphosyntactic classification of social media contents - There are differences on language styles and text quality across social media channels - May hinder the application of NLP techniques to user-generated content C3. Unique user identification based on evolving fingerprint detection - The technique does not depend on technical constraints (JVM or Flash installed) - Future work: cross-device user identification + tracking online-offline activity (IoT) C4. Segmentation of consumers from social media content - Results are highly dependent on specific categories - The proposed techniques have been used in practice in the marketing domain - Future work: detect more demographic and psychographic user characteristics (age, purchasing power, interests, attitudes, etc.) H1 ✔ H2 ✔ H3 ✔ H4 ✔ H5 ✔ H6 ✔ H7 ✔
  41. 41. Dissemination of Results 41 Conclusions and Future Work C2. Morphosyntactic classification of social media contents Óscar Muñoz-García, Carlos Navarro. Comparing user generated content published in different social media sources. In Proceedings of the NLP can u tag #user generated content ?! via lrecconf. org Workshop co-located with LREC 2012 (2012) C3. Unique user identification based on evolving fingerprint detection Óscar Muñoz-García, Javier Monterrubio-Martín, Daniel García-Aubert. Detecting browser fingerprint evolution for identifying unique users. International Journal of Electronic Business (2012) C4. Segmentation of consumers from social media content Silvia Vázquez, Óscar Muñoz-García, Inés Campanella, Marc Poch, Beatriz Fisas, Nuria Bel, Gloria Andreu. A classification of user-generated content into Consumer Decision Journey stages. Neural Networks (2014) Guadalupe Aguado-de-Cea, Mara Auxiliadora Barrios, María Socorro Bernardos, Inés Campanella, Elena Montiel-Ponsoda, Óscar Muñoz-García, Víctor Rodríguez. Análisis de sentimientos en un corpus de redes sociales. In Proceedings of the 31st AESLA International Conference (2014) Óscar Muñoz-García, Jesús Lanchas Sampablo, David Prieto Ruz. Characterising social media users by gender and place of residence. Procesamiento del Lenguaje Natural (2013) Óscar Muñoz-García, Silvia Vázquez Suárez, Nuria Bel. Exploiting Web-based collective knowledge for micropost normalisation. In Proceedings of the Tweet Normalization Workshop co- located with SPLN 2013 (2013) Intl. Workshop Spanish Workshop Intl. Journal Spanish Journal Intl. JCR Spanish Conf.
  42. 42. Thank you for your attention! @omunozgarcia

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