The Language of Brands in Social Media

Applications of the Proposed Approach in Data-Driven Brand Management

Our proposed approach employs the standard approach to mining social media data using topic models and LDA and identifies similarities among brands using both average topic probabilities and significance testing. This approach has various applications to brand management.

Uncovering Key Brand Associations/Topics Using Differential Language Analysis

We evaluate the benefits of understanding brand–topic relationships using differential language analysis (see Appendix A for technical details), which can help brand managers identify top positive and negative topic correlates. To illustrate this further, we use the differential language analysis approach and examine the top five most positively and negatively correlated topics for a small sample of brand names. We include the top five positive predictors in Panel A of Table 2 and the top five negative predictor topics in Panel B. We find some notable similarities and differences across competing brands. For example, in Panel A, both Pizza Hut and Papa John's have the same topic appearing as the most highly correlated topic for both brand names. Similarly, in Panel B, Pizza Hut and Papa John's have the same topic appearing as the least correlated topic for both brand names. The same is true of CNN and Fox News. In this way, by examining patterns of positively and negatively correlated topics, a brand can identify primary points of similarity and differentiation with key competitors.

Table 2. Five Topics Most and Least Likely to Be Mentioned with Brand Names.

Brand	Most Likely Topic 1	Most Likely Topic 2	Most Likely Topic 3	Most Likely Topic 4	Most Likely Topic 5
A: Most Likely to be Mentioned with Brands
Pizza Hut
Papa John's
CNN
Fox News
7 Up
B: Least Likely to Be Mentioned with Brands
Pizza Hut
Papa John's
CNN
Fox News
7 Up

Table 3 provides a list of the top five topics that link to various brands. To further illustrate the use of this information, we highlight how the 7 Up brand's key associations can be identified using topic model outputs and bag-of-words. We show the word clouds for the 7 Up brand in Figure 2.⁶ In each of these word clouds, the sizes of the words indicate the frequency of the words within a topic. As Table 3 shows, 7 Up has two topics linked to the brand that may provide some insights into key brand associations. The first (topic 89) has a list of bag-of-words that has to do with savings, deals, and coupons, indicating that 7 Up is a rather promotion-intensive brand. Brand managers can use this insight to determine whether this is a desirable association for the brand. Second, topic 31 lists several related categories, such as ice, chocolate, drink, bar, and enjoy, which may indicate usage-related associations for 7 Up. Perhaps consumers perceive 7 Up as a complement to other drinks and food. This insight into usage-related associations may also be valuable to managers aiming to manage the brand.

Figure 2. Word clouds pertaining to top five positive predictors (7 Up example).

Table 3. Sample Brand Names and Top Topics: Beverage and Energy Drinks.

Brand Name	Topic Terms	Correlation
7 Up
89	save, coupon, deals, canada, hot, coupons, deal, wipes, baby, dry, ad, low, flavor, pack	.157
31	ice, coffee, drink, cream, chocolate, cold, taste, drinks, cake, favorite, water, cup, bar, enjoy, mix	.080
90	dew, mtn, drink, diet, drinking, coke, soda, bottle, craving, dr, tho, orange, sleep, flavor, starts	.064
47	super, tea, green, bowl, video, spot, bottle, bags, ice, sweet, found, soda, ad, glass, bag	.050
82	music, live, listen, video, song, watch, play, mix, house, story, talk, official, voice, radio, future	.040
Coca-Cola
90	dew, mtn, drink, diet, drinking, coke, soda, bottle, craving, dr, tho, orange, sleep, flavor, starts	.064
58	money, coke, make, spend, life, diet, bottle, give, hours, mine, making, brought, save, lose, design	.054
31	ice, coffee, drink, cream, chocolate, cold, taste, drinks, cake, favorite, water, cup, bar, enjoy, mix	.036
13	case, edition, cover, color, special, bag, foundation, red, limited, moto, matte, eye, turbo, giveaway	.035
9	world, deal, media, social, art, ad, heroes, campaign, real, agree, testing, trade, reach, ads	.032
Dr Pepper
90	dew, mtn, drink, diet, drinking, coke, soda, bottle, craving, dr, tho, orange, sleep, flavor, starts	.076
58	money, coke, make, spend, life, diet, bottle, give, hours, mine, making, brought, save, lose, design	.053
35	love, amazing, show, awesome, _cbs, guys, heard, beautiful, absolutely, gotta, wow, _ind, loved	.051
73	happy, day, birthday, make, people, valentine, hope, shopping, beautiful, made, :), mine, cake, makes, youre	.046
31	ice, coffee, drink, cream, chocolate, cold, taste, drinks, cake, favorite, water, cup, bar, enjoy, mix	.040
Gatorade
90	dew, mtn, drink, diet, drinking, coke, soda, bottle, craving, dr, tho, orange, sleep, flavor, starts	.093
31	ice, coffee, drink, cream, chocolate, cold, taste, drinks, cake, favorite, water, cup, bar, enjoy, mix	.081
47	super, tea, green, bowl, video, spot, bottle, bags, ice, sweet, found, soda, ad, glass, bag	.068
23	water, lot, filter, parking, fits, air, oil, engine, replacement, parts, small, machine, bid, bottle, usa	.056
57	house, home, room, im, dead, bed, walking, sleep, full, bottle, watching, floor, mom, half	.041
Pepsi
90	dew, mtn, drink, diet, drinking, coke, soda, bottle, craving, dr, tho, orange, sleep, flavor, starts	.185
58	money, coke, make, spend, life, diet, bottle, give, hours, mine, making, brought, save, lose, design	.127
47	super, tea, green, bowl, video, spot, bottle, bags, ice, sweet, found, soda, ad, glass, bag	.075
31	ice, coffee, drink, cream, chocolate, cold, taste, drinks, cake, favorite, water, cup, bar, enjoy, mix	.063
43	real, cool, life, big, hit, making, things, thing, pretty, amazing, giving, double, stuff, quick, sports	.051
Powerade
31	ice, coffee, drink, cream, chocolate, cold, taste, drinks, cake, favorite, water, cup, bar, enjoy, mix	.092
90	dew, mtn, drink, diet, drinking, coke, soda, bottle, craving, dr, tho, orange, sleep, flavor, starts	.082
70	drinking, life, miller, millercoors, lite, major, beer, high, network, calls, hard, problem, point, yeah, things	.049
23	water, lot, filter, parking, fits, air, oil, engine, replacement, parts, small, machine, bid, bottle, usa	.045
28	chicken, cheese, pizza, food, sandwich, eat, fries, dinner, made, craving, easy, breakfast, natural, lunch, delicious	.043
Red Bull
90	dew, mtn, drink, diet, drinking, coke, soda, bottle, craving, dr, tho, orange, sleep, flavor, starts	.067
31	ice, coffee, drink, cream, chocolate, cold, taste, drinks, cake, favorite, water, cup, bar, enjoy, mix	.052
70	drinking, life, miller, millercoors, lite, major, beer, high, network, calls, hard, problem, point, yeah, things	.034
57	house, home, room, im, dead, bed, walking, sleep, full, bottle, watching, floor, mom, half	.028
79	good, luck, sounds, pretty, morning, idea, job, awesome, bad, hope, rn, friend, god, roll	.026
Sprite
90	dew, mtn, drink, diet, drinking, coke, soda, bottle, craving, dr, tho, orange, sleep, flavor, starts	.115
82	music, live, listen, video, song, watch, play, mix, house, story, talk, official, voice, radio, future	.083
47	super, tea, green, bowl, video, spot, bottle, bags, ice, sweet, found, soda, ad, glass, bag	.057
31	ice, coffee, drink, cream, chocolate, cold, taste, drinks, cake, favorite, water, cup, bar, enjoy, mix	.057
49	ain, ass, im, ur, bitch, bro, hate, guys, real	.055

Brand Safety and Monitoring Negatively Correlated Topics

A related application of the differential language analysis approach is to identify negative topic correlations that are linked to a brand, which could provide important information critical to brand safety. The term "brand safety" has been used to describe everything from ad fraud and viewability to user experience and adjacency or placement in contextually appropriate environments. This concept of brand safety requires monitoring and tracking of both positive and negative associations of a brand in online settings. For example, a brand may engage in contextual targeting and end up placing ads next to content that is violent or meant for a more mature audience, both of which could pose risks for a brand's reputation. Marketers who are concerned about potential reputational damage due to these content associations may want to track the negatively correlated words and topics of a brand to ensure that the brand not only has distanced itself from such content but also is actually negatively correlated with such content. Our differential language analysis approach allows for this type of analysis because it identifies significant, negative topic correlations for each brand (in addition to positive topic correlations).

Uncovering Brand Similarities Using Topic Probabilities

One of the key benefits of the topic modeling of brand data is the ability to understand brand similarities based on relationships between topics and brands. Topic similarity between brands in various product categories could become a novel approach to identifying and selecting brand alliance partners, as it allows assessing a brand's cultural relevance and identifies a set of partners that occupy a similar space in the cultural conversations of consumers.

The literature on brand partnerships has identified similarity and fit as key factors influencing brand alliance success. Attitudes toward the alliance are enhanced when a high degree of fit exists. Prior research has focused on the importance of fit based on attribute fit, product category, or brand image (Rao, Qu, and Ruekert 1999). We next discuss an approach for identifying similar brands using the output of LDA that relies on average topic probabilities.

The bag-of-words model is a representation of the topic model that highlights key associations for each topic that is linked to a brand. The mean topic probabilities associated with each brand (average across all messages pertaining to a brand) can be used directly to examine which topics are linked to which brands. Using the approach of averaging topic correlations at the brand level, we constructed a correlation matrix of various brands across different categories such as cars, restaurants, and beverages (Table 4, Panels A–C). Table 5 provides a summary of the across-category correlations for a set of beverage brands. Figure 3 presents the same information as a two-dimensional positioning map for the car industry.

Figure 3. Brand positioning map based on topic modeling.

Notes: The positioning map was generated by the brand correlations, which were derived from the average topic probabilities from a 100-topic model (the correlations are shown in Panel C of Table 4).

Table 4. Correlations Among Brands Based on Average Topic Probabilities.

A: Restaurant Brand Correlations
	Applebee's	Arby's	Burger King	Chick-Fil-A	Domino's	Dunkin’ Donuts	McDonald's	Olive Garden	Papa John's	Pizza Hut	Taco Bell	TGI Friday's
Applebee's	1.00
Arby's	.98	1.00
Burger King	.97	.97	1.00
Chick-Fil-A	.97	.97	.96	1.00
Domino's	.93	.95	.92	.93	1.00
Dunkin’ Donuts	.99	.98	.97	.97	.92	1.00
McDonald's	.86	.86	.85	.88	.85	.82	1.00
Olive Garden	.99	.97	.96	.97	.92	.98	.84	1.00
Papa John's	.97	.97	.96	.96	.97	.97	.83	.97	1.00
Pizza Hut	.97	.97	.95	.95	.98	.96	.84	.96	.99	1.00
Taco Bell	.66	.67	.62	.70	.74	.60	.79	.66	.66	.71	1.00
TGI Friday's	.89	.87	.87	.89	.84	.88	.79	.90	.88	.88	.64	1.00
B: Beverage Brand Correlations
	7 Up	Coca-Cola	Dr Pepper	Gatorade	Pepsi	Powerade	Red Bull	Sprite
7 Up	1.00
Coca-Cola	.66	1.00
Dr Pepper	.71	.78	1.00
Gatorade	.75	.75	.81	1.00
Pepsi	.57	.64	.59	.60	1.00
Powerade	.70	.67	.72	.92	.54	1.00
Red Bull	.61	.71	.76	.75	.58	.66	1.00
Sprite	.67	.57	.63	.74	.55	.70	.51	1.00
C: Car Brand Correlations
	Audi	BMW	Buick	Cadillac	Chevrolet	Chrysler	Dodge	Ford	GM	Honda	Hyundai	Jeep	Lexus	Mazda	Nissan	Subaru	Toyota	VW
Audi	1.00
BMW	.21	1.00
Buick	.65	.18	1.00
Cadillac	.81	.33	.75	1.00
Chevrolet	.75	.35	.59	.86	1.00
Chrysler	.77	.36	.64	.87	.96	1.00
Dodge	.64	.19	.64	.68	.53	.58	1.00
Ford	.69	.25	.64	.82	.79	.78	.53	1.00
GM	.51	.40	.47	.68	.78	.78	.42	.62	1.00
Honda	.72	.39	.62	.83	.82	.81	.53	.74	.64	1.00
Hyundai	.59	.66	.46	.73	.83	.82	.42	.65	.74	.75	1.00
Jeep	.87	.25	.72	.93	.83	.84	.73	.74	.64	.79	.62	1.00
Lexus	.85	.38	.74	.93	.82	.84	.69	.78	.65	.85	.74	.91	1.00
Mazda	.56	.33	.40	.63	.75	.70	.29	.58	.72	.75	.67	.62	.62	1.00
Nissan	.63	.20	.53	.73	.62	.61	.52	.53	.52	.58	.49	.72	.69	.42	1.00
Subaru	.26	.15	.14	.25	.32	.29	.12	.18	.30	.36	.29	.29	.27	.44	.18	1.00
Toyota	.80	.28	.67	.84	.86	.85	.57	.83	.63	.77	.67	.84	.85	.60	.63	.24	1.00
VW	.79	.14	.66	.77	.63	.67	0.53	.68	.37	.66	.43	.80	.81	.37	.63	.14	.84	1.00

Notes: We used the average topic probabilities outputted from LDA to construct the correlation matrix (see Method 1).

Table 5. Average Correlations Among Sample Brands and Across Industry Brands (Based on Average Topic Probabilities for 100 Topics).

	7 Up	Coca-Cola	Dr Pepper	Gatorade	Pepsi	Red Bull	Red Lobster	Sprite
7 Up	1.00	.66	.71	.75	.57	.61	.58	.67
Axe	.57	.59	.59	.60	.48	.72	.62	.47
Betty Crocker	.42	.30	.38	.30	.33	.28	.06	.34
Bud Light	.63	.67	.80	.83	.46	.65	.81	.63
Budweiser	.60	.67	.64	.71	.54	.64	.57	.54
Clorox	.51	.51	.48	.49	.32	.45	.43	.31
Coca-Cola	.66	1.00	.78	.75	.64	.71	.64	.57
Coors Light	.57	.60	.69	.69	.39	.53	.73	.51
Dr Pepper	.71	.78	1.00	.81	.59	.76	.76	.63
Gatorade	.75	.75	.81	1.00	.60	.75	.77	.74
Heineken	.65	.71	.77	.77	.50	.66	.78	.61
Hershey's	.66	.64	.71	.70	.49	.59	.50	.56
Huggies	.47	.33	.31	.34	.08	.34	.28	.18
Kraft Foods	.35	.31	.37	.35	.30	.37	.15	.29
Lays	.68	.57	.59	.69	.28	.54	.70	.46
L’Oréal	.24	.36	.40	.20	.24	.41	.07	.21
McDonald's	.65	.64	.79	.79	.40	.71	.85	.62
Monster Energy	.64	.71	.80	.79	.38	.75	.89	.59
Pepsi	.57	.64	.59	.60	1.00	.58	.30	.55
Powerade	.70	.67	.72	.92	.54	.66	.68	.70
Publix	.52	.48	.58	.55	.30	.44	.62	.37
Red Bull	.61	.71	.76	.75	.58	1.00	.64	.51
Red Lobster	.58	.64	.76	.77	.30	.64	1.00	.50
Smirnoff	.72	.67	.71	.81	.57	.61	.57	.69
Sprite	.67	.57	.63	.74	.55	.51	.50	1.00
Starbucks	.59	.64	.76	.75	.36	.66	.94	.49
Stouffers	.43	.46	.55	.60	.29	.45	.56	.44
Taco Bell	.55	.58	.75	.67	.42	.72	.65	.50

Notes: These are average correlations across brands based on topic similarities.

As the correlation matrix in Panel A (restaurant brands) of Table 4 shows, Applebee's is highly correlated with Olive Garden (r = .99), and both represent chain restaurants. Pizza Hut is highly correlated with both Papa John's (r = .99) and Domino's (r = .98), though it also shows strong correlations with restaurants across the board. Of these, Taco Bell has lower correlations across the board with all the restaurants, which is perhaps due to its distinct cuisine of Mexican-inspired foods.

Overall, the pattern of correlations is consistent with what we would expect given the types of restaurants (fast-food vs. chain restaurants) and foods (pizza vs. fast food) that are characterized by these brands. Beyond the product type, these patterns of correlations are reflective of a different type of underlying similarity between brands, based on their cultural relevance to different groups of customers as reflected in the social media language. Similarly, notable insights can be gleaned by examining the beverage brand correlations (Table 4, Panel B) or car brand correlations (Panel C).

We present the second analysis of the beverage brand correlation matrix in Panel B of Table 4. As the panel shows, Gatorade has a high correlation with Powerade (r = .92) and with brands such as 7 Up (r = .75) and Dr Pepper (r = .81). Brand managers could use this information to identify potential new cobranded opportunities featuring brands with high correlations. Table 5 presents correlations of beverage brands across a range of brands in other categories to highlight the benefits of going across categories to identify similar brands. In this example, Gatorade and Smirnoff (r = .81) present a possibility for cobranding, given high correlations of social media conversations. Our analysis of brand correlations based on average topic probabilities reveals new opportunities for collaborations.

Forecasting Shifts in Brand Positioning and Future Brand Preference

Brand–topic probabilities from one year to the next can be used at an aggregate level as an early warning of future shifts in brand preferences. The previous section provides a detailed overview of our measure TTV and also an empirical application of the concept. We show that our measure of topic probability can help predict changes in future brand preference, as well as future lapsed users. To demonstrate the value of this as a meaningful metric, we present some model-free evidence; we compare the brand preference shifts and lapsed users at high and low levels of TTV in our database. At low levels of TTV (brands with the lowest 10% of TTV have scores of .016), we find that the corresponding average shift in future brand preference is 1.83% and shift in future lapsed users is 1.06%. By contrast, for brands with the highest 10% levels of TTV, we find that the change in future brand preference is 8.13% and the change in future lapsed users is 4.29%. This additional model-free evidence provides some indication about using TTV as a predictor of key changes that are likely to occur in the future. Brand managers can use TTV as an early warning system to predict future shifts in brand preference and intervene to limit the likely increase in lapsed customers in the future.

Designing Marketing Communications Using Topic Models

A brand can improve its target audience appeal by basing its marketing communications on the results of topic modeling. By identifying culturally relevant themes that emerge from topics, a brand can situate its marketing communications within these topics or emergent themes, thus improving its target market appeal. Branding scholars have highlighted the benefit of incorporating consumer narratives and stories into the brand to create open-source brands (Fournier and Avery 2011). Consumers can cocreate brand meanings and identities by developing and circulating narratives about the brand, and firms can also facilitate this process by making digital platforms and processes that lead to brand meaning cocreation.

One approach that can help incorporate UGC into marketing communications is by designing brand dashboards that highlight the latest topics that feature a brand. As an illustrative example, we highlight the topics featuring the CNN brand name (see Appendix C). This dashboard contains the following information: (1) the top three topics linked to a brand at any given point in time, (2) shifts in brand topics over multiple years, (3) the words that are linked to a given topic, and (4) brands that feature similar topics. For example, the keywords for topic 30 are linked primarily to sports, and these could form the basis of an advertising campaign that features a sports theme. CNN could use this analysis of the topic conversation topics to identify persistent themes that are linked to the brand over time. When the relationship to a given topic weakens, CNN could use the feedback to evaluate whether the brand is losing its cultural relevance and then design a new advertising campaign or change its marketing communications accordingly. Finally, the list of brands that feature similar topics could help CNN decide which related websites it could leverage to advertise and promote its shows.

New Product Opportunities Using Topic Models

Another useful application of the proposed topic modeling is to identify new product opportunities. The bag-of-words associated with a brand could present novel ideas that stimulate creative new product opportunities. For example, the second most likely topic to appear with Red Bull's topics (in Table 3) is topic 31. The keywords associated with topic 31 include ice, coffee, drink, cream, chocolate, cold, taste, drinks, cake, favorite, water, cup, bar, enjoy, and mix. This indicates that Red Bull could perhaps enter the ice cream or chocolate categories. It could also offer cobranded cocktails or drinks to be served in bars. As the heat map in Table 5 shows, Red Bull could also consider entering into a cobranded partnership with Taco Bell (r = .72). In a similar vein, Dr Pepper could offer a cobranded new product with Coca-Cola (r = .78). Thus, topic similarity is a useful guide to help identify cobranding partners for new product introduction. A high degree of topic similarity is indicative of a higher likelihood that the cobranded new products will be successful.