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WORDS, EMOTIONS and NETWORKS Profiling Social Media Users

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WORDS, EMOTIONS and NETWORKS Profiling Social Media Users WORDS, EMOTIONS AND NETWORKS Profiling social media users Francesca Greco, Andrea Fronzetti Colladon and Alessandro Polli CUSTOMERS AND SOCIAL MEDIA • This study presents a new approach for the profiling of social media users and the identification of virtual communities sharing brand preferences and interests. • Customers who use social media often reveal personal experiences, preferences and emotions • This information can be extremely relevant for companies and sales managers SOCIAL PROFILING Brain structure, mental BLA, BLA BLA BLA BLA!!! functioning and word BLA BLA BLA associations are highly correlated Communication and behavior Social profiling (Laricchiuta et al, 2018) Mental functioning Communication Feeling Behavior Profiling virtual communities’ brand preferences Emotional Text Mining (ETM) Social Network Analysis (SNA) Profiling Social Behavior DATA COLLECTION & ANALYSIS • We collected all the messages in English language containing a sportwear brand’s name during 8 days from Twitter • We obtained a corpus of 90,700 messages • Data analysis: 1. ETM (preprocessing, term selection, cluster analysis, Correspondence analysis) 2. SNA (indegree, outdegree, weighted indegree, weighted outdegree, contribution Index, betweenness centrality, closeness centrality) 3. ANOVA ETM RESULTS • 581 selected keywords allowed the classification of 84% of the documents • We identified 5 virtual communities WHAT MOTIVATE THE SOCIAL MEDIA USER IN TALKING ABOUT THE BRAND? General differences between users: 1. Interest in selling or buying 2. Preference in shopping alone or in group 3. Interest in a specific brand or in several brands 4. Preferences in fashion design or techno design ANOVA Differences are not significant with respect to betweenness centrality SNA RESULTS: BEHAVIOR • We found significant differences in the social behaviors of people belonging to different virtual tribes • Users with more homogeneous tweets were on average more popular (mentioned/retweeted) • Spammers were usually tweeting across tribes • Members of multiple tribes also had a lower closeness centrality • People close to the network core focused their conversations around one specific discourse topic • In general, we found that unity is strength and that tweeting around few specific discourse topics is rewarding in terms of popularity TRIBE 1: GEEKS (28.5%) (ETM) Keyword N. tweet shoe 3705 wear 2317 man 2205 competitor brand 1906 look 1417 check 1387 white 1308 pant 1004 track 898 shirt 892 team 886 The brand is perceived as a valuable sneaker 845 sportwear producer but not the only one. Colors, materials, technology and design are its specificities TRIBE 1: GEEKS (SNA) • They are active: they send messages and cite other users • They are popular • High closeness • All the virtual tribes have more spammers than Geeks TRIBE 2: FASHIONISTAS (10.2%) (ETM) N. Keyword tweet reflective 2201 july 1808 Influencer design 1437 The brand is pair 1245 perceived as a kit 990 fashion designer. reply 809 Users are much Influencer 2 design 787 more interested in competitor brand 547 exclusive designs free 442 than in sport size 398 materials exclusively 382 deal 319 TRIBE 2: FASHIONISTAS (SNA) • They are similar to Geeks but have a lower profile • They are also less active and less popular with a higher percentage of spammers TRIBE 3: SEEKERS (23.9%) (ETM) Keyword N. tweet retail 3723 free 3220 buy 2566 code 2365 size 2345 sale 2157 ultra 1430 breed 1301 discount 1296 steal 1171 User consider the brand as worthy but they fall 1138 like to look for promotions and discounts. drop 1019 They are bargain hunters because it’s so exciting to do it! TRIBE 3: SEEKERS (SNA) • They are the most active • They send messages and mention others • But they are not popular • The percentage of spammers is medium to high TRIBE 4: MERCHANTS (5.3%) (ETM) Keyword N. tweet celebrate 3888 enter 3868 copy 3856 AIO 3818 give_away 3809 renewal 3800 user 3798 Kodai 3792 The brand is perceived as a good and tweet 36 users are much more interested in its clean 27 trading than in its use as a sportwear TRIBE 4: MERCHANTS (SNA) • They are not popular at all • Lowest closeness • They have a high outdegree and they are second to no one in spamming TRIBE 5: SOCIAL (32.1%) (ETM) Keyword N. tweet moving 4435 impact 4409 know 2867 year 2423 today 2337 good 2272 Home 2224 sea 2011 community 2010 goat 1990 Love 1909 Purchasing and sharing experiences is much share 1629 more exciting and convenient than doing it alone TRIBE 5: SOCIAL (SNA) • They are popular • They spam others but only when the purpose is to build a purchase group CONCLUSION • Our findings have important practical implications for companies and brand managers • We offer a new approach for the profiling of social media users, applicable in multiple context • Our study advances research on virtual community THANKS [email protected] [email protected] [email protected].
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