Personality and Individual Differences 71 (2014) 135–139

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Personality and Individual Differences

journal homepage: www.elsevier .com/locate /paid

#babyfever: Social and media influences on fertility desires

http://dx.doi.org/10.1016/j.paid.2014.07.0260191-8869/� 2014 Elsevier Ltd. All rights reserved.

⇑ Corresponding author. Address: Kansas State University, Bluemont Hall 492,Manhattan, KS 66506, United States. Tel.: +1 (785) 532 6850.

E-mail address: lorap@ksu.edu (L.E. Adair).

Lora E. Adair ⇑, Gary L. Brase, Karen Akao, Mackenzie JantschKansas State University, United States

a r t i c l e i n f o a b s t r a c t

Article history:Received 30 April 2014Received in revised form 21 July 2014Accepted 23 July 2014Available online 23 August 2014

Keywords:Family planningFertility decision makingSocial influenceOnline interaction

The current work involves an analysis of the content of 499 tweets containing the hashtag ‘‘#babyfever’’to investigate predicted relationships between certain individual differences (e.g., sex, relationship sta-tus), environmental factors (e.g., exposure to children) and the sharing of ‘‘baby fever’’ experiences.Our sample was almost exclusively female (95%), mostly White (67.5%; 16.2% African American, 9.0% His-panic), and mostly childless (87%) with an average age of 20.52 (SDage = 2.21). As predicted, the majorityof shared experiences of ‘‘baby fever’’ included positive emotional responses, and were associated withthe users’ direct exposure to a baby in their life. Positive mentions of ‘‘baby fever’’ were more likely toinclude mentions of direct exposure to babies. Negative mentions of ‘‘baby fever’’ were associated withperceptions that most of an individual’s peer group seems to be having babies and were more commonamongst tweeters who were not in committed relationships.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction other users and these direct interactions make up approximately

Widespread access to the Internet has changed not only theavailability of information but also the ways that we express our-selves, share ideas, and behave as social creatures. As a result, theuse of online media to connect with others has been on the rise,particularly for teenagers and young adults (Gross, 2004;Lenhart, Purcell, Smith, & Zickuhr, 2010). These new ways of build-ing social ties might be particularly useful for individuals whoexperience discomfort when involved in direct contact with others(Pierce, 2009), and they also provide opportunities to exercisegreater control over the impression of oneself that is presented,compared to what is possible to manage in face-to-face self-presentations (Ellison, Heino, & Gibbs, 2006).

Research on online human interaction has recently begunacross many social media environments, including MySpace(Hinduja & Patchin, 2008), and Facebook (Ellison, Steinfield, &Lampe, 2007). However, Twitter as a social media environmentunder systematic study has only recently been introduced intothe psychology literature (Fischer & Reuber, 2011; Macskassy,2012; Ritter, Preston, & Hernandez, 2014). In this work, we willuse a content analysis technique to explore how fertility desiresare shared in online, Twitter environments. Specifically, this anal-ysis will focus on online expressions of ‘‘baby fever’’ (#babyfever).

Twitter users can generate ‘‘tweets’’ using 140 characters or lessto disclose a variety of topics. Tweets can be directed publicly to

5–10% of users’ overall activity; however, the majority of tweets(68%) are simply statements from the original poster (Macskassy,2012). Twitter as a social community seems to be made up oftwo different types of social networks: a small network of usersthat directly contact one another, and the more active (and denser)network of followers (Huberman, Romero, & Wu, 2008). Communi-cation in this dense network of followers is structured by the use ofhashtags, such as #OverYou; users can conduct searches to revealonly tweets including a specific hashtag, and hashtags can propa-gate ideas throughout a large network of followers for weeks, oreven months (Tsur & Rappoport, 2012). The popularity of Twitterand the expression of attitudes and beliefs among networks of fol-lowers makes it a particularly useful medium to collect data for thepresent study on fertility decision making, utilizing #babyfever.

The term ‘‘baby fever’’ describes a physical and emotional desireto have a baby (Brase & Brase, 2012). Though this phenomenon hasonly recently been studied in psychological research, evidence hasthus far supported that women tend to experience baby fever morefrequently and more intensely than men (Brase & Brase, 2012). Thisfinding informed our first prediction: that females would be morelikely to post tweets including ‘‘#babyfever’’, compared to males.

1.1. Fertility desires and decision making

Stated desires to have a baby are particularly relevant forunderstanding fertility outcomes; desires and intentions to havea baby are predictive of actual fertility outcomes (Miller, Severy,& Pasta, 2004). The connection between fertility desires orintentions and actual fertility outcomes is complicated, however,

136 L.E. Adair et al. / Personality and Individual Differences 71 (2014) 135–139

as people often fail to realize their stated desires in their actual fer-tility outcomes (Toulemon & Testa, 2005).

Historically recent shifts in fertility rates have made the furtherstudy of fertility decision-making particularly pressing, as overallfertility rates are falling below replacement level in many countries(Aarssen & Altman, 2006; Caldwell, Caldwell, & McDonald, 2002).Another recent trend in fertility—particularly seen in industrializedcountries—is the postponement of having children (Berrington,2004). Possible factors influencing these trends include changesin family structure, women returning to the workforce, changesin the working population (Caldwell et al., 2002), and greater focuson acquiring greater social status rather than offspring production(Aarssen & Altman, 2006; Perusse, 1993). In sum, reproductivebehavior is changing, and the sharing of fertility desires throughsocial media provides a unique opportunity to better understandthis process in flux.

To what extent might an individual’s fertility desires be shapedby their social world and the social exchange of information? Pre-vious research has found that fertility desires and intentionsappear to be influenced by information from peers (Hensvik& Nilssen, 2010), the media (Barber & Axinn, 2004; Hornik &McAnany, 2001), and exposure to children in daily life (Brase &Brase, 2012). Social influences on decision making have also beenobserved in several species which rely on the preferences of con-specifics to inform their own decisions about with whom to mate(Gibson & Hoglund, 1992; Place, Todd, Penke, & Asendorpf, 2010)and where to search for food (Galef & Giraldeau, 2001).

We propose that a similarly complex family planning decision –whether or not to have children – may rely upon social informa-tion, a phenomenon we have termed ‘‘baby copying’’. (Hensvik,2010), for example, found that being in close proximity withcoworkers’ who were having children increased the likelihood thatother females would also have children within the next year ortwo. The complex cognitive task of deciding whether or not to havechildren can benefit from reliance upon social information, and wetherefore predict that tweets containing the hashtag ‘‘#babyfever’’will likely include references to exposure to children through one’sacquaintances, friends, or family. Both males and females areaware of their friends’ and family’s fertility preferences and expec-tations (Macklin, 1980); specifically indicating that they experi-ence pressure to become parents and disapproval when they failto meet these expectations to parent (Miles, 2005). Therefore, wepredict that tweets sharing experiences of ‘‘baby fever’’ which indi-cate that most of the users’ peers and friends have children willalso contain mentions of negative affect.

Social information obtained from the media also appears tohave an influence on individual fertility intentions and desires(Barber & Axinn, 2004; Hornik & McAnany, 2001). Prolonged expo-sure to the media is associated with a preference for smaller fam-ilies, more positive attitudes towards contraceptive use (Barber &Axinn, 2004), and an increased likelihood of using contraceptives(Hornik & McAnany, 2001). Interestingly, women’s fertility desiresseem to be more strongly shaped by exposure to media comparedto men’s (Barber & Axinn, 2004).

1.2. Baby fever online

In summary, we believe that data shared through Twitter canprovide insight into users’ attitudes and desires regarding child-bearing. Specifically, an analysis of the content of tweets contain-ing the hashtag ‘‘#babyfever’’ may illuminate certain individualdifferences (e.g., sex, relationship status) and environmental fac-tors (e.g., exposure to children) that are associated with the sharingof ‘‘baby fever’’ experiences. For example, single individuals expe-riencing baby fever may be more likely to share negative attitudesor feelings regarding this experience, since the absence of a com-

mitted partner may serve as a barrier to the realization of their fer-tility desires. The current work expands upon recent research onthe phenomenon of ‘‘baby fever’’ (Brase & Brase, 2012), by lookingat the following hypotheses within a social media environment:

H1: Females will be more likely to post tweets which includethe hashtag #babyfever

H2: Tweets with #babyfever will often contain clear emotionalvalences, and these emotional reactions will be elicited by par-ticular content

H3: Positive emotional reactions in #babyfever tweets are pre-dicted to be elicited by:

a. Exposure to babies, pregnant women, and baby-relateditems (either directly or via media)

b. Family, friends, or acquaintances having a babyc. Images depicting babies and baby-related stimuli

H4: Negative emotional reactions in #babyfever tweets are pre-dicted to be elicited by the perception that ‘‘everyone’’ (largeportions of one’s peer group) is having babies

H5: Unattached (single) individuals that mention #babyfeverwill be more likely to include negative emotional reactions(e.g., envy, regret, jealousy), as compared to individuals inromantic relationships

2. Methods

A sample of 499 tweets containing #babyfever were recordedduring the fall of 2012. This sample size was determined basedon a convenience sampling of the available tweets obtained in asearch of the hashtag ‘‘#babyfever’’ during the fall of 2012. Whentweets were recorded, raters also collected any available demo-graphic information available on the Twitter user’s personal profile(i.e., the relationship status of the user, biological sex, race/ethnic-ity, and place of origin). Of the available data, our sample wasalmost exclusively female (95%), mostly White (67.5%; 16.2% Afri-can American, 9.0% Hispanic), and mostly childless (87%) with anaverage age of 20.52 (SDage = 2.21; see Fig. 1).

After tweets were recorded, two independent raters that wereblind to the hypotheses coded each tweet on the following dimen-sions: if the tweet mentioned exposure to babies, exposure to preg-nant women, exposure to baby-related items, babies in the user’sfamily, a friend’s or acquaintances’ baby, the perception that‘‘everyone’’ seems to be having babies, positive emotional valence,negative emotional valence, exposure to babies in the media, inclu-sion of a baby-related image, or themes of envy, regret, or jealousy(see Fig. 2). After all 499 tweets were coded on these dimensions,the two raters met to discuss and resolve any coding disagree-ments – mutual agreement was reached in all cases of differentialcoding through brief discussions.

3. Results

As predicted, we found that females were (overwhelmingly)more likely than men to use the #babyfever hashtag, and also thatthese tweets using #babyfever were often connected to strongemotional reactions. Positive emotions regarding tweeted experi-ence of ‘‘baby fever’’ (52.3%; 261/499) were significantly morecommon than negative emotions (15.2%; 76/499; v2 (1) = 101.56,p < .001). Interestingly, experiences of ‘‘baby fever’’ were particu-larly likely to include mentions of direct exposure to babies (e.g.,‘‘When the cook brings her baby grandson to the house. . . Everyonehas #babyfever’’; 33.5%; 167/499) This was in contrast to relativelylow levels of noting exposure to babies through media outlets (e.g.,‘‘I’m crying watching @Kourtney Kardash deliver Mason for like the

Fig. 1. Frequency distribution for the themes and content found in tweets containing #babyfever.

Fig. 2. Frequency distribution for the available demographic information of users publishing tweets containing #babyfever.

L.E. Adair et al. / Personality and Individual Differences 71 (2014) 135–139 137

100th time. I’m so pathetic. #babyfever’’; 7.4%; 37/499), or evenexposure to pregnant women (e.g., ‘‘I love baby bellies & touchingthem. #omfg. #babyfever #adorable’’; 8.2%; 41/499). However,mentions of direct exposure to babies were equally likely toinvolve babies in the individual’s family (15.0%; 75/499) as theywere to involve an individuals’ friend or acquaintance’s baby(15.2%; 76/499). For a quick summary of the content found in thecurrent sample of tweets including ‘‘#babyfever’’, see Fig. 2.

3.1. Emotional reactions to baby fever

To address our hypotheses about the events which elicited par-ticular emotional reactions associated with ‘‘baby fever’’, bivariate

correlations were calculated (Table 1). Consistent with our hypoth-eses, #babyfever tweets which were positively emotionally valen-ced were significantly more likely to include mentions of directexposure to babies (r = .261⁄⁄), and babies being born to familymembers (r = .211⁄⁄). There were not significant associations, how-ever, between positive emotional reactions and exposure to preg-nant women, baby-related items, or exposure to babies in themedia. On the other hand, some of these contents did show aninverse correlation with negative emotional reactions (exposureto babies: r = �.100⁄, exposure to pregnant women: r = �.107⁄,babies in the family: r = �.131⁄). Consistent with our hypothesisabout negative emotional reactions, perceptions that large portionsof one’s peer group (‘‘everyone’’) are having babies was negatively

Table 1Bivariate correlations for content and themes found in tweets containing #babyfever,and specifically which themes were likely to occur in tandem.

Positive Negative Envy/regret/jealousy

Exposure to babies .261** �.100* �.101*

Exposure to pregnant women .008 �.107* �.064Exposure to baby-related items .043 �.038 �.111*

Exposure to babies in the media �.067 .050 .006Babies in family .211** �.131* �.037Friend’s/acquaintance’s baby .081 �.040 �.126*

Belief that ‘‘everyone’’ is havingbabies

�.220** .122* .052

Inclusion of a baby-related picture .166** �.155** �.129*

Note: For the above Pearson’s r bivariate correlations (all n = 499).* p < .05.

** p < .001.

138 L.E. Adair et al. / Personality and Individual Differences 71 (2014) 135–139

correlated with positive reactions (Table 1; r = �.220⁄⁄) and posi-tively correlated with negative reactions (r = .122⁄).

Finally, tweets with #babyfever that expressed positive emo-tional reactions were more likely to include a baby-related picture(r = .166⁄⁄), whereas tweets that expressed negative emotionalreactions were less likely to include a picture (r = �.155⁄⁄; Table 1).These results are again consistent with our hypotheses.

3.2. Baby fever demographics

Although 95% of users sharing experiences of ‘‘#babyfever’’were female (474/499), when males’ did share their experiencesof ‘‘#babyfever,’’ these mentions never contained negative emo-tional themes (female expressions of negative emotional valence:75/474; 15.8%, compared to male expression of negative emotionalvalence: 0/20). This was a significant sex difference (v2 (1) = 3.73,p = .053). Additionally, individuals sharing their experiences of‘‘baby fever’’ were significantly more likely to also be involved ina romantic relationship (16.6%; 83/499) than to be single (2.4%;12/499; v2 (1) = 53.06, p < .001). As hypothesized, unattached (sin-gle) individuals that mentioned #babyfever were be more likely toinclude negative emotional reactions (e.g., envy, regret, jealousy;5/12), as compared to individuals in romantic relationships (10/83; Fisher’s exact test, p = .021). This is consistent with the ideathat the lack of a committed romantic partner may serve as a bar-rier to realizing an individual’s fertility desires – single individualstweeting about #babyfever would be more likely to mention envy,regret, or jealousy, compared to individuals in relationships.

4. Discussion

Our content analysis of 499 tweets containing the hashtag,#babyfever found that the majority of shared experiences of ‘‘babyfever’’ were from female users, included positive emotionalresponses, and were associated with the users’ direct exposure toa baby in their life. Positive mentions of ‘‘baby fever’’ were morelikely to include mentions of direct exposure to babies (but notas likely to be elicited by seeing baby-related items or pregnantwomen) and images of babies and baby-related items. Negativeemotional reactions in the contexts of ‘‘baby fever’’ were associatedwith perceptions that most of an individual’s peer group seems tobe having babies. Negative responses, such as envy, regret, or jeal-ousy, were also more common amongst tweeters who were not incommitted relationships and female tweeters (none of the male-sourced tweets using #babyfever displayed negative emotionalresponses).

These results support nearly all aspects of our hypotheses, withthe notable exception that positive emotional reactions were not

significantly associated with more distal exposures to babies (i.e.,baby-related items, pregnant women, friends/acquaintances hav-ing babies). Instead, the primary elicitors of (positive reactionsto) baby fever are direct exposure to babies and news of babieswho are actually related to oneself (e.g., ‘‘I swear I could kiss thesecheeks till they fell off! I love my niece so much she makes mewant another’n! #babyfever’’). We believe that these individualsmay be using their direct experiences with babies as a source ofsocial information indicating that now might be a good time topursue their own reproductive goals. Indeed, complex social deci-sions can often be improved (e.g., be more efficient and accurate)when an individual decision-maker relies upon the choices of thosearound them (Eva & Wood, 2006; Sumpter & Pratt, 2009;Waynforth, 2007). It such a phenomenon as ‘‘baby copying’’ trulyexists, it appears to be tuned relatively finely to direct experienceand family members’ fertility, rather than more broadly to anybaby-related events.

The findings that #babyfever tweets expressing the belief that‘‘everyone’’ seems to be having babies were associated with morenegative emotional responses is also an intriguing result. Themajority of individuals in our sample were female, single, young(Mage = 20.52) and childless (87.8%, or 438/499, did not have anyinformation indicating parenting experiences in their Twitter pro-file). It is reasonable to suggest that this subpopulation is aware ofthe social stigma associated with remaining childless. Indeed, pre-vious work has demonstrated that both men and women are awareof the social stigma associated with remaining childless (Macklin,1980; Miles, 2005); furthermore, women may begin to experiencea change in sexual desires and behaviors in response to decliningfertility (the ‘ticking biological clock’) as early as their mid-latetwenties (Easton, Confer, Goetz, & Buss, 2010). Several of our col-lected tweets expressed negative emotions regarding remainingchildless (e.g., ‘‘Sometimes I feel like I’m the only one who doesn’thave a baby yet. #babyfever’’). Direct exposure to babies, monitor-ing of peers’ fertility activities, and perceived social pressures tobecome a parent, may act in concert to influence fertility desiresand intentions.

In a related manner, single individuals sharing their experiencesof ‘‘baby fever’’ were more likely to express negative emotions,such as longing and discouragement, about their fertility desires(e.g. ‘‘I just want a baby to cuddle with, is that too much to ask?#babyfever #singleproblems’’). This is consistent with previousstudies which have documented relationship status as one of thestrongest and most consistent predictors of fertility intentions(Forrest, 1994; Schoen, Astone, Kim, Nathanson, & Fields, 1999;Williams, Abma, & Piccinino, 1999). It is possible that the absenceof a romantic partner constitutes a ‘‘deal-breaker’’, such that whenthis condition is not met, individuals will not (or cannot) pursuetheir fertility plans, and this situation elicits negative affect in indi-viduals who desire to have a baby. However, it is worth noting thatconclusions regarding the relationship between relationship statusand emotions included in expressions of baby fever are limited,given that few Twitter users included in the current study pub-lished information regarding their relationship status (95/499),and only 12 of those users indicated that they were single.

These conclusions are limited by the observational nature of ourstudy design, and the constraints of such a design. For example, wecannot probe more deeply into the intentions and motivations thatare expressed by the tweeters. But the present study also demon-strates the potential for using online social media as a rich windowinto human attitudes and desires, and the communication of theseattitudes and desires untainted by the environment of a formalsurvey study. As more human interactions are mediated by onlinemediums such as Twitter, our understanding of social phenomenamust involve a closer look at how and what information is sharedonline. The current work offers insight into how experiences of

L.E. Adair et al. / Personality and Individual Differences 71 (2014) 135–139 139

baby fever are shared through online social networks. It would beuseful to have more work on fertility decision making that manip-ulates the relevant social information people to which people areexposed (e.g. social expectations and pressures), and evaluateshow that information influences individuals’ fertility desires andintentions. Future work might employ experimental designs thatcontrol the type of fertility-relevant social information participantsare exposed to, perhaps comparing the effect of direct exposure tobabies with the effect of exposure to pregnant women and baby-related items on reported fertility desires and intentions. Further-more, future work might compare the effect of direct exposure tobabies with the effect of exposure to images of babies (like thosefound on a Twitter or Facebook news feed) on fertility desiresand intentions.

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