In order to communicate brand concepts and values to the young generations, many brands are highly active on social media platforms such as Twitter, Facebook, and Instagram. Brand-Generated Content has already become the most common marketing strategy for fashion brands and plays a significant role in influencing consumers’ purchase intention. With increased competition on social media platforms, companies need to understand which posting features can bring in more consumer engagements such as number of likes and comments on social media platforms. In this paper, we develop (1) a model to predict the number of likes and (2) a methodology to detect anomaly of posts that have unusually high percentage of negative user comments based on Instagram design variables, including semantic text meanings, facial expressions, color scheme and background of photos, and post timing, among others. We collected a data set of brand-generated Instagram posts from ten fashion brands. The data covers the image, text, and user comments posted between 2019 and 2020. Image features were extracted using Convolutional Neural Network, and text topics were generated through Latent Dirichlet Allocation. Our results will help managers design Instagram posts to increase consumer engagement and to reduce negative consumer reactions.

Introduction Social media marketing is an attractive marketing method for fostering relationships with customers. About 30% of social media users find social networking sites important when searching for information about brands as well as showing their support towards them (Nielsen, 2017). This engagement with brands on social media is one of the factors driving company outcomes. For example, consumer engagement in social media brand communities is found to have a positive impact on purchase spending (Goh, Heng, and Lin, 2013), brand equity (Christodoulides and Jevons, 2012), and brand attitude (Schivinski and Dabrowski 2016). Consumer engagement involves both consumer interaction and co-creation of the content (Smith and Gallicano, 2015). In order to enhance engagement with brand content, marketers must persuade consumers to interact with those messages by sharing, commenting or liking them (Chang, Yu, Lu, 2014). Hence, interaction is the crucial step towards improving consumer engagement. While marketers rely on experimenting in order to find elements that drive consumer interaction, researchers use vast social media data in order to examine relationships between brand message characteristics and consumer interaction with those messages. For example, (Vries, Gensler, and Leeflang, 2012) studied the impact of post’s vividness, interactivity, content, position of a post and valence of comments on brand post popularity as represented by number of likes and comments. Other researchers such as (Wang et al ,2016) examined the impact of topic, tone and the length of post on social media engagement defined not only by the number of likes and shares, but also by the likability of characters featured in the post. Chang, Yu, and Lu (2014) studied how argument quality, post popularity, and post attractiveness can lead to consumer engagement. Similarly, (Lee and Hong, 2016) investigated the impact of emotional appeal, informativeness and creativity of a message on positive consumer behavior towards brand message. However, little is known so far about the effects of the frequency and spacing of brand-generated content on the dynamics of consumer interaction. Advertising research shows that advertising frequency has an impact on various consumer behavior and attitude outcomes and suggests that there is an optimum level of exposure to advertising that yields greatest results (Schmidt and Eisend, 2015; Broussard, 2000). Moreover, research on advertising repetition in traditional channels suggests an inverted u-shape relationship between ad repetition and message effectiveness. This happens because at a certain number of exposures negative factors, such as boredom and irritation (Heflin and Haygood, 1985), kick in and overweigh positive ones. As a result, the effectiveness of an ad starts diminishing. This effect is also known as the wear-out effect. On the other hand, (Lee, Ahn, and Park, 2015) suggest that inverted U-shape relationship between repetition and attitude towards the brand does not hold true in online environments. This is the case because users can control their exposure to advertising, therefore they do not expose themselves to the ad to the extent that they feel adverse toward it. As firm-generated brand content on social media is a form of advertising, it is interesting to examine, whether wear-out effect occurs in the context of social media and user interaction. In addition, the effect of advertising repetition is found to depend on the time period, or space, between ad exposures (Janiszewski, Noel, and Saywer, 2003). Spacing between exposures affect learning (Sawyer, Noel, and Janiszewski, 2009), attitude towards the brand (Schmidt and Eisend, 2015), purchase spending (Sahni, 2015), attrition rate and customer response (Dreze and Bonfrer, 2008). Moreover, recent study by (Wang, Greenwood, and Pavlou, 2017), who investigated the influence of posting on the propensity to unfollow the brand on the largest social media in China WeChat, found that posting leads to higher likelihood of unfollowing the brand, which in turn has a negative effect on the long term sales. However, WeChat may be considered to be more intrusive than Facebook because of the differences in how followers get notified about new brand posts. Therefore, it is interesting to examine whether the same effect of posting holds true on Facebook. Finally, viral marketing research suggests that the growth rate of interaction with the content depends on the rate of creation of other messages (Karnik, Saroop, and Borkar, 2013). Based on the findings from previous studies, it is evident that frequency and spacing may have a significant influence on the level and growth rate of user interaction. Furthermore, two-sided advertising research suggests that inclusion of negative information in product related messages can yield better results in terms of persuasive power than if no negative information is included (Eisend 2006). In addition, political communication researchers found that sentiment-carrying Twitter messages tend to be retweeted more often and more quickly (Stieglitz and Dang-Xuan, 2013). Therefore, it is suggested that the effect of message frequency and spacing on the level of consumer interaction is moderated by the sentiment of the message. In other words, the optimal level of message frequency is expected to be higher for emotionally-charged firm-generated brand messages as compared to neutral ones. Hence, the following research questions are proposed: RQ1: How does frequency and spacing of brand-generated content affect the dynamics of consumer interaction on social media and how is this effect moderated by the sentiment of the content? RQ2: How does posting on social media affect the unfollowing by brand followers? RQ2a: Does the spacing between messages help reduce the negative effect of posting on the unfollowing by brand followers (if such effect is present)? Research Design & Theoretical Development In order to answer these questions two data sets were gathered via Facebook’s API consisting of 6,471 and 932 brand posts respectively. Two separate data sets were needed to examine the frequency effects on the overall level of consumer interaction as well as on the growth rate of interaction. Therefore, post and page data for 7 international brands from 5 different product categories for the period of 2 years were collected to examine the frequency effects. To investigate the effect of posting on the growth rate of consumer interaction, 11 brands were tracked for the 7-week period in order to capture the development of the interaction. In addition, the impact of posting on the propensity to unfollow the brand was examined. Consequently, three separate regression models were built to test the hypotheses. Results showed that frequency of posting and the level of consumer interaction has an inverted u-shape relationship and that the level of consumer interaction is positively influenced by the space between the posts. Further, findings suggest that posting on social media is positively associated with unfollowing by followers and that the growth rate of interaction of the post depends on the rate of new message generation by the same brand. The conceptual model is presented below. Result and Conclusion The study has few theoretical and practical contributions. Answering to the call for research (Vries, Gensler, and Leeflang, 2012) to include the dynamic aspects of interaction, this study contributes to the social media literature by examining the effects of the rate of new message generation on the growth rate of interaction of the post. In addition, this study adds to the stream of research on the wear-out effects in online environments by including higher number of exposures and by testing the type of firm communication (social media communication) that previously has not been studied. Finally, this study contributed to the recent research (Wang, Greenwood, and Pavlou, 2017) by examining the effect of posting on the unfollowing by brand followers. As for practical contributions, findings of this study have implications for marketing managers with respect to the frequency and spacing of posting. This study provides evidence for a more moderate posting strategies in terms of frequency.