Relationship Characteristics on Social Network Sites
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Presentation of a study on the topic: "The mediating and moderating role of relationship characteristics in determining information value on social network sites"
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Relationship Characteristics on Social Network Sites
- 1. THE MEDIATING AND MODERATING ROLE OF RELATIONSHIP CHARACTERISTICS IN DETERMINING INFORMATION VALUE ON SOCIAL NETWORK SITES by Ksenia Koroleva 6th of December, 2013
- 2. MOTIVATION • Facebook is the largest database of social information. Each day: 2.7 billion likes 300 million photos • Stream communication allows users to get a lot of information in a short time • Filtering algorithms ensure that users get the right information • Rationality: is the information we want the information that we need? 2
- 3. CAUSES AND COSEQUENCES OF INFORMATION OVERLOAD: A QUALITATIVE STUDY CAUSAL CONDITIONS Information Longing [20] Keeping in touch [15] STRATEGIES AND ACTIONS Information Characteristics Contact Facilitation [9] passive Social Capital [7] AMOUNT - detail [17] - frequency [27] DRIVING CONDITIONS cognitive HEURISTICS [36] CONSEQUENCES OMISSION [7] VALUE - novelty [47] - interest [24] UNDERSTANDABILITY [7] Network Characteristics INFORMATION OVERLOAD PERCEPTION COGNITIVE [47] AFFECTIVE [30] CONATIVE [15] RELATIONSHIP - strength [45] - attraction [11] - intensity [7] DISTANCE [15] INTERVENING CONDITIONS Time Pressure [20] Social Pressure [11] NETWORK - size [16] - structure [6] Technology [9] FAILED ACTION [11] active HIDE [15] people/ information DELETE [11] people/ information DEACTIVATE account [5] Impact on Newsfeed ACTIVITY [17] Impact on Newsfeed ATTITUDE [21] Change in INFORMATION LOAD [12] Change in INFORMATION QUALITY [4] Influence on SOCIAL CAPITAL [10] DIS-/ SATISFACTION [13] advanced ex-ante NETWORK CONTROL [5] REVERSAL [4] Skills/Knowledge [6] control of SELFBEHAVIOR [9] Koroleva, K., Krasnova, H. and Günther, O. 2010. ‘Stop Spamming Me!’ – Exploring Information Overload on Facebook, in Proceedings of the Americas Conference on Information Systems (AMCIS 2010), AIS Electronic Library, Paper 447. 3
- 4. SOCIAL CONTEXT CUES IN SNS Relational cue Verbal indicator Referrals/Tags Profile Information Recent activity and interests Post Type Time and Place Friends & Network Overlap ‘Likes’ Comments 4
- 5. DETERMINANTS OF INFORMATION VALUE Information Value Relationship Characteristics Similarity Tie Strength Information Characteristics Social Information Media Type Ratings Photos Comments Links Text Which information do users value on SNS? How do relationship and information characteristics interact with each other? 5
- 6. SOCIAL INFORMATION Def.: Social Information – statements and interpretations of others in the social environment Ratings/’Likes’ • Non-verbal cues • Positive emotions • Socially acceptable behavior Comments • Higher effort to process • Negative emotions • No shared context Salancik and Pfeffer 1978 Schmitz and Fulk 1991 6 Schöndienst and Dang-Xuan 2012
- 7. TIE STRENGTH Def.: Tie Strength – frequency and depth of interaction (Mardsen and Campbell 1984) • • • • Limited non-verbal cues Alternative channels Low cost of maintenance Diversity of information • • • • Easy transfer of information Tacit information Relevant information Frequent communication Granovetter 1982 Hansen 1999, 2002 7 Carpenter 2003
- 8. HOMOPHILY/SIMILARITY Def.: Homophily – tendency for friendships to form between those who are alike in some designated respect (Lazarsfeld and Merton 1954) - Value Homophily vs. Status Homophily • • • • More trustworthy Less effort in processing Increases with tie strength Affective relationships Heterophilous ties: • Diverse • Complementary • Instrumental relationships McPherson et al. 2001 Lazarsfeld and Merton 1954 8 Rivera et al. 2010
- 9. STUDY DESIGN • Facebook application Objective data collected automatically (multimedia type, comments, likes) Subjective data through a survey (information value, tie strength, similarity) • Sample • 141 users (52% female & 48% male, age mean: 27) • Each person evaluates up to 6 posts (5.88 on average), randomly selected from the Newsfeed • In total, 851 observations 9
- 10. INFORMATION VALUE Affective Value Evaluation of the information per se, its value 6 pt ordinal scale: Like very much – dislike very much % of sample dislike very much 3.64% dislike 9.17% slightly dislike 17.86% slightly like 36.08% like 23.97% like very much 9.28% 100.00% Feelings, emotions evoked by the information 6 pt ordinal scale: Very useful – very useless • • Cognitive Value Correlated (0,62) BUT! can be empirically distinguished % of sample very useless 24.32% useless 21.74% slightly useless 17.16% slightly useful 22.21% useful 10.46% very useful 4.11% 100.00% 10
- 11. TIE STRENGTH VS. SIMILARITY • Homophily is stronger in closer relationships, correlation (0,5) • BUT! Can be empirically distinguished tie strength weak strong % of sample 7% 0% 7% hardly anything in common 20% 2% 22% something in common 34% 17% 51% quite a lot in common 4% 11% 16% very much in common 0% 4% 4% 66% 34% 100% nothing in common similarity 11
- 12. MODERATION WITH TIE STRENGTH Affective Likes Cognitive Photos (w.r.t.status) Links (w.r.t.status) Tie Strength 0.050 (0.010)*** -0.012 -0.016 (0.008) Comments 0.050 (0.010)*** (0.008)** 0.314 0.374 (0.108)*** (0.110)*** -0.033 0.379 (0.086) (0.088)*** -1.927 (0.126)*** -1.185 (0.103)*** -0.482 (0.096)*** 0.559 (0.096)*** -0.630 (0.105)*** 0.075 (0.104) 0.593 (0.105)*** 1.439 (0.113)*** 1.550 (0.109)*** 2.247 (0.134)*** 0.160 (0.035)*** 0.04 0.250 (0.040)*** 0.05 Affective Value 0.051 (0.010)*** -0.007 (0.008) 0.317 (0.109)*** 0.022 (0.086) 0.596 (0.084)*** Cognitive Value 0.050 (0.010)*** -0.011 (0.008) 0.368 (0.110)*** 0.436 (0.089)*** 0.520 (0.084)*** Likes*Tie Strength Comments*Tie Strength Photos*Tie Strength Links*Tie Strength _cut1 _cut2 _cut3 _cut4 _cut5 rho R 2 Affective Value 0.069 (0.013)*** -0.015 (0.009)* 0.131 (0.139) -0.085 (0.107) 0.335 (0.174)* -0.055 (0.021)*** 0.027 (0.016)* 0.547 (0.234)** 0.352 (0.190)* -1.810 Cognitive Value 0.059 (0.013)*** -0.020 (0.009)** 0.222 (0.144) 0.392 (0.110)*** 0.264 (0.179) -0.025 (0.021) 0.029 (0.016)* 0.402 (0.236)* 0.132 (0.194) -0.501 (0.123)*** -1.717 -0.417 (0.130)*** (0.111)*** (0.140)*** -0.966 0.305 -1.055 0.221 (0.109)*** (0.110)*** (0.120)*** (0.123)* -0.242 0.836 -0.329 0.753 (0.103)** (0.113)*** (0.115)*** (0.126)*** 0.843 1.705 0.759 1.623 (0.106)*** (0.122)*** (0.118)*** (0.135)*** 1.879 2.538 1.805 2.456 (0.121)*** (0.144)*** (0.132)*** (0.154)*** 0.171 0.248 0.164 0.239 (0.035)*** (0.040)*** (0.035)*** (0.040)*** 0.9 0.10 0.10 0.11 Random effects Ordered Probit, N=851 * p<0.1; ** p<0.05; *** p<0.01 12
- 13. MODERATION 1: LIKES*TIE STRENGTH NB! the values of the dependent variable displayed on the plot are calculated based on the assumption of a continuous dependent variable (not ordinal) 13
- 14. MODERATION 2: COMMENTS*TIE STRENGTH NB! the values of the dependent variable displayed on the plot are calculated based on the assumption of a continuous dependent variable (not ordinal) 14
- 15. MODERATION WITH SIMILARITY Likes Comments Photos (w.r.t. status) Links (w.r.t. status) Similarity (centered) Likes*Similarity Affective 0.050 (0.010)*** -0.012 (0.008) 0.314 (0.108)*** -0.033 (0.086) Cognitive 0.050 (0.010)*** -0.016 (0.008)** 0.374 (0.110)*** 0.379 (0.088)*** Affective 0.045 (0.010)*** -0.007 (0.007) 0.307 (0.110)*** -0.032 (0.086) 0.597 (0.047)*** Cognitive 0.044 (0.010)*** -0.013 (0.008) 0.335 (0.111)*** 0.397 (0.088)*** 0.502 (0.048)*** Comments*Similarity Photos*Similarity Links*Similarity _cut1 _cut2 _cut3 _cut4 _cut5 rho R2 Affective 0.043 (0.010)*** -0.007 (0.008) 0.309 (0.110)*** -0.024 (0.086) 0.441 (0.101)*** 0.003 (0.011) 0.002 (0.008) 0.325 (0.122)*** 0.235 (0.105)** Cognitive 0.043 (0.010)*** -0.012 (0.008) 0.316 (0.113)*** 0.397 (0.088)*** 0.272 (0.107)** -0.003 (0.011) 0.010 (0.008) 0.391 (0.130)*** 0.257 (0.110)** -1.927 (0.126)*** -1.185 (0.103)*** -0.482 (0.096)*** 0.559 (0.096)*** 1.550 (0.109)*** 0.160 (0.035)*** -0.630 (0.105)*** 0.075 (0.104) 0.593 (0.105)*** 1.439 (0.113)*** 2.247 (0.134)*** 0.250 (0.040)*** -2.088 (0.130)*** -1.297 (0.105)*** -0.530 (0.096)*** 0.622 (0.096)*** 1.735 (0.113)*** 0.141 (0.034)*** -0.676 (0.104)*** 0.069 (0.103) 0.622 (0.105)*** 1.534 (0.114)*** 2.418 (0.139)*** 0.229 (0.039)*** -2.086 (0.129)*** -1.293 (0.104)*** -0.525 (0.095)*** 0.628 (0.096)*** 1.752 (0.113)*** 0.135 (0.034)*** -0.674 (0.104)*** 0.074 (0.102) 0.626 (0.104)*** 1.541 (0.114)*** 2.431 (0.139)*** 0.218 (0.039)*** 0.04 0.05 0.20 0.18 0.22 0.19 Random effects Ordered Probit, N=851 * p<0.1; ** p<0.05; *** p<0.01 15
- 16. MODERATION 1: PHOTOS * SIMILARITY NB! the values of the dependent variable displayed on the plot are calculated based on the assumption of a continuous dependent variable (not ordinal) 16
- 17. MODERATION 1: LINKS * SIMILARITY NB! the values of the dependent variable displayed on the plot are calculated based on the assumption of a continuous dependent variable (not ordinal) 17
- 18. MEDIATION Likes Affective 0.050 Cognitive 0.050 Affective 0.051 Cognitive 0.050 Affective 0.045 Cognitive 0.045 (0.010)*** Photos (w.r.t. status) Links (w.r.t. status) Tie Strength (1-strong, 0-weak) Similarity (centered) _cut1 _cut2 _cut3 _cut4 _cut5 rho 2 R (0.010)*** (0.010)*** (0.010)*** (0.010)*** (0.010)*** -0.012 -0.016 -0.007 -0.011 -0.007 -0.012 (0.008) Comments (0.008)** (0.008) (0.008) (0.008) (0.008) 0.314 0.374 0.317 0.368 0.308 0.336 (0.108)*** (0.110)*** (0.109)*** (0.110)*** (0.110)*** (0.111)*** -0.033 0.379 0.022 0.436 -0.022 0.408 (0.086) (0.088)*** (0.086) (0.089)*** (0.087) (0.089)*** 0.596 0.520 0.110 0.115 (0.084)*** (0.084)*** (0.094) (0.096) 0.568 0.470 (0.053)*** (0.055)*** -1.927 (0.126)*** -1.185 (0.103)*** -0.482 (0.096)*** 0.559 (0.096)*** 1.550 (0.109)*** 0.160 (0.035)*** -0.630 (0.105)*** 0.075 (0.104) 0.593 (0.105)*** 1.439 (0.113)*** 2.247 (0.134)*** 0.250 (0.040)*** -1.717 (0.130)*** -0.966 (0.109)*** -0.242 (0.103)** 0.843 (0.106)*** 1.879 (0.121)*** 0.171 (0.035)*** -0.417 (0.111)*** 0.305 (0.110)*** 0.836 (0.113)*** 1.705 (0.122)*** 2.538 (0.144)*** 0.248 (0.040)*** -2.042 (0.135)*** -1.251 (0.112)*** -0.483 (0.104)*** 0.671 (0.105)*** 1.787 (0.122)*** 0.144 (0.035)*** -0.627 (0.113)*** 0.120 (0.111) 0.673 (0.114)*** 1.586 (0.123)*** 2.472 (0.146)*** 0.229 (0.039)*** 0.04 0.05 0.09 0.10 0.20 0.18 Random effects Ordered Probit, N=851 * p<0.1; ** p<0.05; *** p<0.01 18
- 19. MEDIATION Tie Strength 0,048 (0,09) 0,933 (0,06)*** Affective Value 0,565 (0,05)** Similarity Sobel test statistic: 0,528 (0,06)*** Similar effect with Cognitive value Proportion of the effect that is mediated: 0,92 affective; 0,83 cognitive 19
- 20. SUMMARY: 5 COMMANDMENTS 1. Similarity: I like it because I’m like you! 2. Likes: I like it because everyone likes it 3. Comments create information overload (need more explanation!) 4. A picture is worth a thousand words 5. I don’t necessarily like links, but they can be useful, especially if I’(m) like you! 20
Editor's Notes
- http://news.cnet.com/8301-1023_3-57498531-93/facebook-processes-more-than-500-tb-of-data-daily/ http://www.google.de/imgres?sa=X&biw=1680&bih=946&tbm=isch&tbnid=xZr8-4T0ZwTbkM:&imgrefurl=http://mezmer.blogspot.com/2012/02/searching-for-red-stockings-myth-of.html&docid=-sPIG67-mCcFAM&imgurl=http://4.bp.blogspot.com/-LT0WZWzt7Rg/T3h9U1j4qqI/AAAAAAAAAkA/5fDiqXDmM6o/s640/infooverload.jpg&w=368&h=276&ei=hQ2fUp7ABIas4AS9mIGIAQ&zoom=1&iact=rc&dur=556&page=1&tbnh=135&tbnw=187&start=0&ndsp=52&ved=1t:429,r:6,s:0,i:101&tx=109&ty=53 “….The real source of information overload…. is the stuff we like, the stuff we want. And as filters get better, that's exactly the stuff we get more of.It's a mistake, in short, to assume that as filters improve they have the effect of reducing the information we have to look at. As today's filters improve, they expand the information we feel compelled to take notice of. Yes, they winnow out the uninteresting stuff (imperfectly), but they deliver a vastly greater supply of interesting stuff. And precisely because the information is of interest to us, we feel pressure to attend to it. As a result, our sense of overload increases.”
- CMC was criticized due to the lack of contextual cues. However, on SNS there are a lot of contextual cues, which we subdivide into: contextual information, relational and social information. Contextual information: Although in traditional CMC it was possible to attach documents to the E-mails, on SNS various types of content can be shared more effectively – pictures, links and videos. These alternative types of information help to transfer information more effectively (compare the effort necessary to process pictures as opposed to text), as well as transmit a lot of tacit information – information that is difficult to put in words – critical to establish shared meaning. At the same time, verbal indicators can also convey contextual information : lexical surrogates provide for informal communication, whereas various linguistic cues can express immediacy of communication (Walther 1992). SNS users use a lot of linguistic surrogates, such as hearts and smileys that help to transfer their emotions and intentions.What is most unique in SNS are referrals can call the attention of other users to the information that is shared and thus ensure that the information reaches the people to whom it provides most value (Burt 2002). At the same time, each post on SNS is saved with time and place which provides additional cues to the recepients.Social information or feedback from others. Social information is defined as the perceptions of others in the social environment about the information that is shared (Fulk et al. 1987), It also serves as the feedback from others on the information they receive. Through feedback mechanisms provided by the platform, individuals learn from others (Bandura 1977) and adjust their behaviors accordingly (Bandura 1963). Ratings are standardized mechanisms that allow users to evaluate their perceived value of information on a standardized scale, such as one-sided affirmations (e.g. Facebook ‘like’), binary decisions (e.g. Digg up or down), or along a continuum (Amazon’s 1-5 stars). On such SNS as Facebook mainly one-sided affirmations are used, as providers fear propagation of negative feedback. Comments are open-ended mechanisms that allow individuals to register their more elaborate opinions on certain digital content. Although these two types of feedback are sequential, ratings from other users are akin to non-verbal responses (such as thumbs up or down) and therefore might partially play the role of concurrent feedback. Especially the one-sided ratings (likes) signal to the receiver that there is a certain number of users who have understood and agree with the information shared. Although in the following we mainly focus on social information, contextual and relational information are assessed in the paper as well. SNS, however, might challenge this proposition by providing a lot of contextual information to its users. Contextual information allows to process information more quickly and effectively (Dennis and Kinney 1998), as well as establish the shared meaning quicker (Miranda and Saunders 2003). Moreover, a lot of the information shared on SNS is ambiguous without understanding its context, where the role of contextual cues for interpreting this information becomes particularly acute.
- Social Information: Social information stemming from the behavior, statements, interpretations and cognitive assessments by others in the social environment (Schmitz and Fulk 1991) provides cues for users to develop their attitudes, opinions and even needs (Salancik and Pfeffer, 1978). As opposed to rational choice models such as media richness, the social influence model (Schmitz and Fulk 1991) postulates that individual perceptions about media are, in part, socially constructed, as they are impacted by the different evaluations of media characteristics provided by others.Ratings:. EASY TO PROCESS.Thus, they make certain information more salient, by providing cues about which information to consider (Salancik and Pfeffer 1978). Non-verbal cues:Specifically, ratings are similar to nonverbal cues, such as a thumbs up or down, and thus provide instant impressions of the information being shared while also promoting awareness of other people who interact with the content on the platformSocially acceptable behavior: If presented with the opinions of others about something that are in consent, people agree (Chaiken et al. 1989). Herding: If everyone likes it, I like it too”. Positive emotions expressed in the post (Schöndienst and Dang-Xuan 2012)Comments Comments instead are akin to verbal responses, which must be processed extensively. Although they provide more elaborate evaluations of shared information, they might cause information overload and are therefore likely to exhibit a negative association with information value. First, their verbal nature and the inability to summarize comments as effectively as ratings may create an information overload. Because SNS users often contribute comments simultaneously and are not limited in the number of verbal symbols they use, more information gets exchanged on the network (Dennis 1996; Schultz and Vandebosch 1998). More contributors tend to lead to decreasing marginal value (Asvansund et al. 2004; Schroder et al. 1967); that is, additional comments require similar amounts of information processing but produce less insight. As a form of verbal feedback, comments can clarify content, complete a statement, or express a controversial opinion (Dennis and Kinney 1998). Empirical evidence suggests that on SNSs, high numbers of comments imply negative emotions expressed in the post (Schöndienst and Dang-Xuan 2012). Therefore, information that sparks many comments likely signals controversy and may decrease its overall credibility. Third, users might be reluctant to rely on the opinions of others who are not similar to them (Salancik and Pfeffer 1978) or with whom they do not interact frequently (Erickson 1988). Those who comment usually represent the social network that surrounds the source of information, which increases the probability that the respondent who is evaluating the information is not part of this network or is only weakly connected to it. HOWEVER: the impact depends on how people process information. If information is processed systematically, comments will have a negative impact. If information is processed heuristically, then they might have a positive impact on information value.
- Many studies have investigated the influence of tie strength—defined as the frequency and depth of interaction (Mardsen and Campbell 1984)—on the individual ability to obtain valuable information from a social network. more likely to find valuable information about job searches through weak ties than strong ties (Granovetter)novel information and more diversified information. Burt 1992The advent of IT-enabled communication networks (E-mail) lead the researchers to believe that weak ties have more value in these networks (Constant et al. 1996, Pickering and King 1995). Researchers initially considered weak ties because: they are primarily capable of communicating relatively thin types of content, such as text (Daft; Pickering, Orlikowski)and do not transmit a lot of contextual cues. Alternative channels for communication with strong ties, therefore prefer SNS for weak ties Low cost of maintenance on SNS – can communicate with people simultaneously, or just have others in the network: SNS were originally related to increased in both bridging and bonding social capital, but bonding later was disproven.SNS features that undermine this: Richer forms of communication, multimedia and hypermediaSupport existing relationships rather than new onesStrong Arguments:- First, strong ties are associated with better transfer of information (Hansen 1999). Social media platforms provide features that automate many aspects of the search process (e.g. NewsFeed, Twitter lists), lowering the value of weak ties for information search.Strong ties, on the other hand, are more willing to share information and to devote their time to assist one another thus creating a favorable environment for information transfer (Coleman 1988, Uzzi 1996). Second, strong ties are also particularly helpful for transferring tacit information, information that is difficult to put into words (Hansen 2002).Moreover, strong ties possess knowledge about who knows what and requires which information and therefore are more valuable in exchanging information (Uzzi 1997, Hansen 1999). Research suggests that in situations with abundant information, people prioritize information provided by strong ties (Carpenter 2003). NB! Channel expansion theory***But more frequent communication among parties also might enable them to develop a shared understanding of the medium and thereby functionally expand the capacity of these channels for rich communication (Carlson and Zmud 1999).***The later empirical evidence finds that both the diverse network of weak ties and a high bandwidth of communication with strong ties can provide users with diverse and non-redundant information, depending on the environment surrounding these ties (Aral and Van Alstyne 2011). Recent findings: the later empirical evidence finds that both the diverse network of weak ties and a high bandwidth of communication with strong ties can provide users with diverse and non-redundant information, depending on the environment surrounding these ties (Aral and Van Alstyne 2011). However, the relative informational value of strong versus weak ties may depend on other factors, such as the pursued goal (Hansen 1999), type of information (Uzzi 1997), task (Rowley et al. 2000), or organizational structure (Reagans and McEvily 2003; Oh et al. 2006).
- Self-similar alters are more likely to accept them, to be trustworthy, to hold beliefs that affirm their own, thereby mitigating potential conflicts, misunderstandings and monitoring costs that come with making connections. These benefits appear to increase with the strength of the relationship. “Homophily causes ignorance,” Burkeman adds that it tends to make people more extreme. The internet can increase the effect, allowing dittoheads of various persuasions to “exist almost entirely within a feedback loop shaped by your own preferences.”*Digitally mediated contexts of social interaction may impart homophily bias in people’s social choices by selectively exposing people to self-similar others. Facebook could easily offer a list of the People You’re Least Likely To KnowPeople seek out others whom they believe to have valuable and complementary task-related skills.
- tablewith t-test!
- Likes are positively related to information value, however more so for weak ties. For strong ties, the addition of likes does not matter, however for weak ties, it increases the value of information considerably and compensates for the missing tie strength.
- Comments are negatively related to information value. Comments of strong ties are neutral (or even slightly positive), whereas comments on posts from weak ties decrease their value. Signals information overload.
- Photos are preferred from everyone, but especially from those with high similarity.
- Links generally lower than photos, but compared to status are better, especially from similar people.
- A model and the results of the Sobel test
- UPDATE!!!