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We describe the effect of social media advertising content on customer engagement using data from Facebook. We content-code 106,316 Facebook messages across 782 companies, using a combination of Amazon Mechanical Turk and Natural Language Processing algorithms. We use this dataset to study the association of various kinds of social media marketing content with user engagement - defined as Likes, comments, shares, and click-throughs - with the messages. We find that inclusion of widely used content related to brand-personality - like humor and emotion - is associated with higher levels of consumer engagement (Likes, comments, shares) with a message. We find that directly informative content - like mentions of price and deals - is associated with lower levels of engagement when included in messages in isolation, but higher engagement levels when provided in combination with brand-personality related attributes. Also, certain directly informative content, such as deals and promotions drive consumers' path-to-conversion (click-throughs). These results persist after incorporating corrections for the non-random targeting of Facebook's EdgeRank (News Feed) algorithm, so reflect more closely user reaction to content, rather than Facebook's behavioral targeting. Our results suggest there are benefits to content engineering that combines informative characteristics that helps obtain immediate leads (via improved click-throughs) with brand-personality related content that helps maintain future reach and branding on the social media site (via improved engagement). These results inform content design strategies. Separately, the methodology we apply to content-code text is useful for future studies utilizing unstructured data such as advertising content or product reviews.

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Data Availability: Some of the Rescuetime data (who accessed sites and did not comply with study) is unable to be shared as it contained identifying information including personal email addresses and social networking site membership details. The University of New England Human Research Ethics Committee gave permission for us to provide data to reviewers but not to share this publicly, as this was not on our original ethics application form. Data are available from the UNE Human Research Ethics Committee at ethics@une.edu.au or by contacting the corresponding author. The contact person is below: Mrs Jo-Ann Sozou Research Officer University of New England Armidale, NSW 2351 Tel: (+612) 6773 3449

There was some attrition between the phases. One hundred and nine participants completed Phase 1 and installed RescueTime on their phone. Of these, ninety-seven completed the remaining phases. However, RescueTime detected 19 who did not fully comply with the SNS vacation and had to be excluded from the dataset leaving a final sample of 78 (40 experimental, 38 control) who fully completed the study. There were 19 males and 19 females in the control condition, and 16 males and 24 females in the experimental condition.

Participants were then rank ordered on the AUS dimension and, starting from the highest score and working down, every 2nd individual was assigned to the experiment condition and all others to the control condition, thus ensuring these groups were equivalent on the AUS. The experimental group were blocked from SNS for one week and asked to temporarily remove Facebook and Instagram applications from their phones, whereas those in the control condition were told they could continue to use SNS normally and would have the opportunity to take their SNS vacation at a later date. Any SNS use on registered devices during this time was detected with the RescueTime application. Participants completed the post-test survey at the end of the vacation period.

Correlations were computed to test the hypothesized relationships between SNS usage amount, usage style, life satisfaction and affective well-being. Then, moderations were conducted to test the effects of the SNS vacation , an IV, on life satisfaction and affective well-being, the DVs, which we expected would be improved among individuals with a low AUS (more passive users) and reduced among those with a higher AUS (more active users). More precisely, the DVs were the changes from pre-test (T1) to post-test (T2), computed by subtracting the score at T1 from that at T2, which was done for the three DVs, life satisfaction, positive affect, and negative affect, with separate moderations run for each. The small sample size could not accommodate two moderators, so we used the composite AUS as a moderator, rather than including active and passive usage as separate moderators. Hence, the IVs of both moderations were (a) being in the experiment or control condition for an SNS vacation (condition), (b) AUS, and (c) AUS condition. In addition, gender and SNS usage at baseline were included as control variables.

RescueTime recorded, on average, 449 minutes (SD = 43.6) of SNS usage during the baseline monitoring week, with a range from 3 to 1664 minutes. The distribution was positively skewed; median usage was 192 minutes (mode = 5.6). SNS usage at baseline did not differ significantly between the Experimental and Control groups (tlog-transformed SNS usage amount = -.41, p = .69).

The results (Table 3) revealed a significant interaction of experimental condition and usage style on PA and a marginally significant interaction of experimental condition and usage style on NA (p = .07). There were no significant effects on life satisfaction. Breaking down the interaction effect on PA, the biggest change was observed in the experimental condition, such that PA decreased from T1 to T2 for more active users, opposite to that hypothesized, and showed little change for more passive users (Fig 1), where we hypothesized a decrease. There was little change in PA for control group participants. Simple slopes analysis (Figs 1 and 2) revealed a significant negative relationship between condition (control vs experimental) and PA change for more active users. For the more passive users, there was no significant effect of the SNS vacation on positive affect change.

Previous studies found that active SNS usage related to increased PA and life satisfaction (subjective well-being) whereas passive usage and more frequent usage related to decreased PA and life satisfaction (see Verduyn [14] for a review). Based on this, people who engage in mainly passive SNS usage could be expected to benefit from an SNS vacation, but people with a more active usage style would not. We tested the effects of a one-week vacation from Facebook and Instagram together, to provide a more complete SNS vacation than taking a break from just one SNS alone. We also circumvented issues of self-report by using software to monitor and block Facebook and Instagram usage, and controlled social desirability effects in reporting life satisfaction by using different questions at pre- and post-test. Participants were recruited from three different countries, and so the findings are not confined to just one national context.

The results revealed a moderation effect of usage style, such that taking a vacation from Facebook and Instagram decreased PA for more active users, and not for more passive users. There was also a small effect on NA, such that NA improved for passive users in the control group, and not the experimental group. There were no significant effects on life satisfaction.

Like the current study, Hinsch and Sheldon [24] found that an SNS break (Facebook and online gaming) resulted in decreased PA. This was not found by Vanman et al. [26], nor by Tromholt [25]. In the current results, decreased PA resulting from the SNS break was restricted to more active SNS users. Active users build and maintain social capital and consequently increase their self-esteem and SWB through SNS use [1, 16], therefore it is an integral part of their lives. Hence, they most likely depend on SNS to maintain and develop their social ties, which may explain the decrease in PA in this study. As such, highly active users may have a level of dependency on SNS. Hormes, Kearns, and Timko [37] found evidence of disordered use of SNS among 9.7% of an American university cohort. If this is elevated among active SNS users, the proportion of addicted active users could be quite high. We believe this is an important direction for future research. This effect was also visible in the overall positive correlations between active usage and life satisfaction and PA.

There were several limitations to this research. Participants volunteered because they wanted to take a break from SNS. This improved the ecological validity of the study, as people would usually take an SNS break voluntarily. However, it also created a possibility of self-selection effects. For example, our participants may have been high in self-monitoring propensity, meaning that they could have a personality characteristic(s) that is different from the general population. The current results will generalize best to similar situations, where people choose to take a break from SNS. Having said this, Hinsch and Sheldon [24] found similar effects in their two studies, one of which used self-selected volunteers, the other of which allocated participants to condition as part of their course requirements. Thus, self-selection (or not) does not appear to be of critical importance in the research design. 2351a5e196