When Do People Trust Their Social Groups?

TL;DR

This study develops a comprehensive framework for predicting trust in social groups, based on individual traits and group characteristics, using survey and behavioral data from Facebook Groups users.

Contribution

It introduces a new predictive model for group trust that incorporates individual trust propensity and group structural features, validated with large-scale data.

Findings

Trust propensity correlates with trust in groups.

Smaller, older, and more homogeneous groups are trusted more.

Group structure and individual position predict trust and friendship formation.

Abstract

Trust facilitates cooperation and supports positive outcomes in social groups, including member satisfaction, information sharing, and task performance. Extensive prior research has examined individuals' general propensity to trust, as well as the factors that contribute to their trust in specific groups. Here, we build on past work to present a comprehensive framework for predicting trust in groups. By surveying 6,383 Facebook Groups users about their trust attitudes and examining aggregated behavioral and demographic data for these individuals, we show that (1) an individual's propensity to trust is associated with how they trust their groups, (2) smaller, closed, older, more exclusive, or more homogeneous groups are trusted more, and (3) a group's overall friendship-network structure and an individual's position within that structure can also predict trust. Last, we demonstrate how group trust predicts outcomes at both individual and group level such as the formation of new friendship ties.

Figures

Tables

Table 1. Table 1. Trust in groups survey items. Participants reported the degree to which they agreed or disagreed to each of the survey items on a five-point Likert scale.

General Attitudes Towards Others
Disposition to trust		Most people can be trusted.
General social support		There are people in my life who give me support and encouragement.
General risk attitude		I’m willing to take risks.
General in-group loyalty		I would describe myself as a “team player”.
Trust in a Group
Care	Other members of the group care about my well-being.
Reliability	Other members of this group can be relied upon to do what they say they will do.
Integrity	Other members of this group are honest.
Risk-taking	I feel comfortable sharing my thoughts in this group.

Table 2. Table 2. Descriptive summary of survey measures, including general attitudes and trust in groups. Sparklines represent the histogram of each measure. (N=6,383)

Variable	Mean	SD	Correlations
			1	2	3
General Attitudes Towards Others
1. Disposition to trust	3.33	1.07
2. General risk attitude	3.68	0.99	0.18***
3. General in-group loyalty	4.54	0.80	0.23***	0.16***
4. General social support	4.34	0.84	0.24***	0.18***	0.36***
Trust in Groups
1. Care	3.90	1.08
2. Reliability	4.05	0.98	0.62***
3. Integrity	4.20	0.95	0.60***	0.67***
4. Risk-taking	4.09	1.06	0.59***	0.54***	0.56***
Note: ∗p0.05; ∗∗p0.01; ∗∗∗p0.001

Table 3. Table 3. Baseline model predicting trust in groups using demographics, disposition to trust, risk attitude, in-group loyalty, and social support. (N=6,323 after removing missing age and gender observations)

	Dependent variable:
	Trust in groups composite score
	(1)	(2)	(3)	(4)	(5)
(Intercept)	4.07∗∗∗	3.51∗∗∗	3.18∗∗∗	2.46∗∗∗	1.98∗∗∗
	(0.04)	(0.05)	(0.06)	(0.07)	(0.08)
Age	$-$0.001∗	$-$0.003∗∗∗	$-$0.002∗∗	$-$0.001∗	$-$0.001∗
	(0.001)	(0.001)	(0.001)	(0.001)	(0.001)
Female	0.09∗∗∗	0.06∗∗	0.08∗∗∗	0.05∗	0.02
	(0.02)	(0.02)	(0.02)	(0.02)	(0.02)
Disposition to trust		0.19∗∗∗	0.17∗∗∗	0.13∗∗∗	0.11∗∗∗
		(0.01)	(0.01)	(0.01)	(0.01)
Risk attitude			0.09∗∗∗	0.07∗∗∗	0.05∗∗∗
			(0.01)	(0.01)	(0.01)
In-group loyalty				0.21∗∗∗	0.16∗∗∗
				(0.01)	(0.01)
Social support					0.19∗∗∗
					(0.01)
Adjusted R2	0.003	0.06	0.07	0.11	0.14
Note: ∗p.05; ∗∗p.01; ∗∗∗p.001

Table 4. Table 4. Five sets of group-level features used for predicting trust in groups.

Feature Set	Features
Basic Properties (5)	Group size, privacy type, group tenure, number of admins/moderators
Category (5)	Self-reported group category
Activity (6)	Group-level and participant-group-pair level time spent, number of posts, number of likes or comments
Homogeneity (3)	Diversity of group age, gender, and similarity between participant and group average
Structural (5)	Network density, average clustering coefficient, participant degree centrality, cliquishness of participant’s friends in the group, average number of mutual friends with group members

Full text

Trust, groups, communities, Facebook

††journalyear: 2019††copyright: acmlicensed††conference: CHI Conference on Human Factors in Computing Systems Proceedings; May 4–9, 2019; Glasgow, Scotland UK††booktitle: CHI Conference on Human Factors in Computing Systems Proceedings (CHI 2019), May 4–9, 2019, Glasgow, Scotland UK††price: 15.00††doi: 10.1145/3290605.3300297††isbn: 978-1-4503-5970-2/19/05††ccs: Human-centered computing Social networking sites

1. Introduction

Trust contributes to the success of social groups by encouraging people to interpret others’ actions and intentions favorably, thereby facilitating cooperation and a sense of community (Gambetta, 1988; Misztal, 2013; Uzzi, 1996; Bachmann, 2001; Dirks, 1999; Preece and Maloney-Krichmar, 2005). In groups, trust increases member satisfaction and task performance (Walther and Bunz, 2005), reduces conflict (Gambetta, 1988; Walther and Bunz, 2005), and promotes effective response to crisis (Meyerson et al., 1996).

Previous research has examined how different factors such as size (Brewer, 1991; Denters, 2002; Zelmer, 2003), group cohesiveness (Hogg, 1993), and activity (Walther and Bunz, 2005) may impact people’s trust in their social groups, both online (Holtz et al., 2017) and offline (Rotter, 1971). However, previous studies tend to be small in scale, limited to specific contexts (e.g., online marketplaces), or only consider a specific type of group (e.g., organizations (Mayer et al., 1995; Colquitt et al., 2007)). Studies that address these three limitations may enrich our understanding of how trust is formed in social groups more generally.

In this work, we build on this rich prior literature on trust to present a framework for predicting an individual’s trust in a social group, and examine how differences at the individual and group levels predict that trust. We focus our analysis on Facebook Groups, a Facebook feature that “allows people to come together to communicate about shared interests” (Facebook, 2018). As of May 2018, 1.4 billion people used Facebook Groups every month (Perez, 2018). By combining a survey ($N$=$6,383$ valid responses) of individuals using Facebook Groups with aggregated behavioral logs, we are able to investigate, across a diverse sample, how an individual’s trust in a group relates to characteristics of the individual, the group, and the individual’s membership in that group.

The survey asked individuals about their general attitudes towards others and trust towards a Facebook group that they were a member of. While prior work has shown that an individual’s general propensity to trust others influences their trust in a particular group (Boss, 1978; Butler Jr, 1999; Ridings et al., 2002; Ferguson and Peterson, 2015), we additionally examine the role of other individual-level differences (e.g, general attitudes towards risk-taking).

We combine these survey results with aggregated behavioral and descriptive data on Facebook Groups. This allows us to study the role of five categories of features that characterize either the group or the respondent’s relationship with the group, based on prior literature: (1) basic properties of the group (e.g., size, membership privacy policy) (Kraut and Fiore, 2014), (2) group category (Denson et al., 2006), (3) group activity (Kraut and Fiore, 2014), (4) group homogeneity (Moser et al., 2017), and (5) the friendship-network structure of the group  (Holtz et al., 2017).

We find that these variables robustly predict participants’ trust in a particular group, with both individual and group characteristics contributing predictive value (adjusted $R^{2}$=$0.26$). In particular, an individual’s trust in a group was most strongly predicted by their general perceived social support, the group’s average clustering coefficient, and their degree centrality in the group. We also show that trust in a group can be estimated using only observational data.

While these results support previous findings showing that intragroup trust decreases with increasing group size and increases with membership restriction (Zelmer, 2003; Denters, 2002; Brewer, 1991; La Macchia et al., 2016; Moser et al., 2017), we find that these trends only hold up to a certain point. When the size of a group exceeds 150 members (roughly Dunbar’s number, or the expected cognitive limit beyond which social relationships are difficult to maintain (Dunbar, 1992)), the membership policy of the group (public v.s. private) ceases to play a predictive role. Moreover, in deciding how much to trust a group, we show that group size matters less to individuals with a higher general propensity to trust.

Further, previous work suggests that people trust groups in which they are more active (Cartwright and Zander, 1953), but we find that only certain types of activities are associated with trust: people “like” and “comment” more in groups they trust but do not necessarily post more, suggesting that trust is associated more with directed communication than with information sharing.

Finally, we show that trust in groups is associated with both individual- and group-level outcomes. Increased trust leads to individuals being more likely to form friendships with other members of the group, but is also associated with the group being less likely to grow larger in size.

In summary, we

(1) present results of a large survey of individuals’ trust attitudes towards their social groups,

(2) examine how characteristics of both the individual and group contribute to trust in a group, and

(3) show how this trust affects future individual- and group-level outcomes.

A deeper understanding of how these factors collectively contribute to trust in groups can better equip communities to foster trust among their members.

2. Background

To begin, we describe previous work on trust and the factors that impact it. This prior work also motivates the multilevel approach that we take for studying trust in social groups.

2.1. What is Trust?

Trust has been defined as a belief in the reliability of others (Gambetta, 1988). Previous work on trust can be roughly organized into examining trust

(1) as a personal trait (i.e., a propensity to trust others),

(2) with respect to a specific other individual (i.e., dyadic trust), or

(3) with respect to multiple others (e.g., in groups or organizations).

These differences roughly correspond to how trust is conceptualized across disciplines—as arising from individual traits in psychology (Rotter, 1971), as calculable using game theory in economics (Williamson, 1993), or from relationships among people in sociology (Granovetter, 1985). In this broad literature, trust has typically been either measured using surveys (Survey, 2018; Rotter, 1971), qualitative interviews (Six and Sorge, 2008), or through economic games that measure how much money people entrust others with (Berg et al., 1995). In the present work, we measure trust using a survey asking about trust and its various correlates, and combine this with observed behavioral data.

2.1.1. Trust and the individual

One line of work has studied trust as an individual characteristic, similar to a personality trait, and suggests that trust is rooted in life experiences and societal norms (Rotter, 1971; Bowlby, 1969; Ainsworth et al., 2015). In this context, trust is referred to as “generalized trust” (Nannestad, 2008), a “propensity to trust” (Ferguson and Peterson, 2015), or a “disposition to trust” (Wu et al., 2010). A propensity to trust others has been associated with being older, married, having higher family income and college education and living in a rural area, but not with gender (Taylor et al., 2007; Paxton, 2007). Work has also studied cross-country differences in an individual’s propensity to trust (Bjørnskov, 2007). In this work, we refer to this “individual trait” definition of trust as a “disposition to trust”, which we measure in order to explain trust in groups. To minimize cross-cultural effects, we focus on U.S.-based individuals. A disposition to trust is also related to other personal traits, such as risk-taking  (Cook et al., 2005), feelings of social support, and group loyalty (Barrera Jr and Ainlay, 1983; Van Vugt and Hart, 2004). We include these relevant concepts in our work.

2.1.2. Trust between individuals

At a dyadic level, trust can be modeled using social exchange theory (Berg et al., 1995; Homans, 1958; Blau, 1964; Emerson, 1976), where people are assumed to be rational actors who maximize their own benefits in interactions with others. Trust is then defined as the decision to undertake risks in these exchanges. Another significant line of work has examined dyadic trust in online settings. This work suggests that reputation  (Yamagishi et al., 2009; Resnick and Zeckhauser, 2002; Kuwabara, 2015; Qiu et al., 2018), homophily (Abrahao et al., 2017), and the language used in online profiles (Ma et al., 2017) mediate an individual’s trust in someone else.

2.1.3. Trust in groups and organizations

While it is useful to study social interactions at dyadic level, many interactions take place in the context of groups, both offline and online. Significant work has studied how trust influences an organization’s structure, productivity, and cohesiveness (Mayer et al., 1995; Nyhan, 2000; McEvily et al., 2003; Fine and Holyfield, 1996). Trust in organizations was positively associated with improved job performance, citizenship behavior (e.g., altruism and courtesy), and greater cooperation. It is negatively associated with counterproductive activities such as disciplinary action and tardiness (Dirks, 1999; Colquitt et al., 2007). Trust in online groups impacts various outcomes including member satisfaction, information sharing, and task performance in virtual teams (Walther and Bunz, 2005), but is also shown to be fragile and temporal when the team is formed around a common task with a finite life span (Jarvenpaa and Leidner, 1999; Meyerson et al., 1996).

More recently, several studies have looked at trust in Facebook groups, mostly in the context of buy-and-sell groups (Holtz et al., 2017; Moser et al., 2017). Qualitative research on Facebook buy-and-sell groups showed that trust can be fostered through mechanisms such as closed membership and sanctioning  (Moser et al., 2017), and our quantitative analysis here confirms some of these findings.

2.1.4. Multilevel perspectives on trust

Given the diversity of approaches used to study trust, some work has called for “multilevel perspectives” on trust (Rousseau et al., 1998) that account for trust at the individual, group, and organizational levels. Research has proposed models of how trust in others depends on a disposition to trust (Mayer et al., 1995). And as previously noted, studies have also examined how trust at the individual level may mediate trust at other levels (e.g., in a specific community (Ridings et al., 2002) or in groups (Boss, 1978; Ferguson and Peterson, 2015)). Nonetheless, such work remains largely theoretical. Empirical studies have tended to be small-scale and only examine a subset of the many characteristics and behaviors of individuals and groups that may mediate trust. In this work, we show how trust may be better modeled by considering individual and group-level features together in a generalizable framework.

2.2. Determinants of Trust in Groups

What contributes to trust in groups? Here we review relevant literature that guide the selection of our feature sets in predicting trust in groups.

2.2.1. Individual differences

Trust in groups can be mediated by one’s disposition to trust others, as it correlates with one’s initial intentions to trust a group, especially in ambiguous situations (Gill et al., 2005). A disposition to trust can positively impact trust in different settings, including trust between individuals (Yakovleva et al., 2010), in communities (Ridings et al., 2002), in organizations (Kantsperger and Kunz, 2010), or in online services (Wu et al., 2010). Similarly, a disposition to trust increases trustworthiness evaluations given to Airbnb hosts (Ma et al., 2017), though in other settings, a disposition to trust was not associated with trust in peer sellers (Jones and Leonard, 2008).

Past work also suggests an inverse relationship between risk aversion and trust (Abrahao et al., 2017)—the more comfortable an individual is with taking risks, the higher the trust they have in groups.

Further, prior literature treats membership of voluntary associations as an indicator of trust (Putnam, 2000, 1993). Thus, greater in-group loyalty, as well as perceived social support from others, should both be linked with higher trust in groups due to increased group participation and perceived social capital.

2.2.2. Group characteristics

Trust in groups may also stem from basic properties of the group such as its size (Brewer, 1991; Denters, 2002; Zelmer, 2003). For instance, experiments have shown that people identify more strongly with smaller groups (Simon and Brown, 1987). Older groups should also be trusted more, as they have more time to mature in norm and management. Past research has also described how secrecy can build community (Fine and Holyfield, 1996) and shown that group cohesiveness promotes trust (Stokes, 1983). Recent qualitative work on Facebook Groups also suggests that by making membership exclusive and screening new members, trust can be enhanced (Moser et al., 2017).

Homogeneity, which relates to cohesiveness, may also contribute to trust. People tend to be closer to and trust others who are similar to them (McPherson et al., 2001). Research has also found a relationship between gender and age homophily and increased trust (Ahmad et al., 2011; Abrahao et al., 2017).

Higher levels of group activity are also linked with greater trust (Cartwright and Zander, 1953; Walther and Bunz, 2005). Increased social interaction provides “opportunities for people to get acquainted, to become familiar with one another, and to build trust” (Ren et al., 2007), thus leading to higher familiarity, and in turn, greater trust (Gulati, 1995).

2.2.3. Network characteristics

Beyond group characteristics mentioned above, the overall structure of relationships between individuals in the group, as well as the individual’s embeddedness the group’s social network may mediate trust. A person’s number of friends and the connections among these friends can both increase the likelihood of them joining a community (Backstrom et al., 2006; Ugander et al., 2012). As dense networks promote cooperation and social norms, they are also likely to be associated with increased trust (Coleman, 1988). In buy-and-sell groups on Facebook, network density and the degree centrality of the seller are positively correlated with an intention to transact, which may signal higher trust in the group (Holtz et al., 2017). Our work uses similar features but directly measures trust via a survey, and considers the role of network features within a much large set of variables.

This rich prior literature motivates our analysis in this work, in which we conduct a large-scale survey and analyze behavioral data to show how individual- and group-level differences help predict trust in groups. Our research questions are as follows: (a) Can a baseline model that accounts only for individual attitudes predict trust in groups? (b) What is the relative contribution of the different sets of group-level features (basic group properties, group category, activity, homogeneity, and structural properties) on trust in groups beyond the baseline model?

3. Methods

In this work, we conducted a survey of 10,000 respondents to a random sample of active Facebook Groups users in the U.S. People were invited to participate in the survey via an ad on Facebook. The survey was designed to measure both individual attitudes as well as trust in one of the randomly selected Facebook groups of which they were members. We augmented this survey data with self-reported demographic data such as age and gender and server logs of these individuals’ activity and friendships on Facebook. All log data was de-identified and analyzed in aggregate on Facebook’s servers; researchers did not view any identifiable data nor access any specific posts in any groups. The study was approved by an internal Facebook board as well as Cornell’s Institutional Review Board under protocol #1805008006.

3.1. Sampling

The survey was issued to unique individual-group pairs. We used the following sampling strategy to identify eligible survey candidates. First, we identified Facebook groups that had at least five members and that had a majority of their members located in the U.S. We then identified people in the U.S. who belong to at least one of these groups, and that had at least one interaction (e.g., creating, liking, or commenting on a group post) in the past 28 days. We then sampled eligible individual-group pairs, de-duplicating by both individual and group at random. The sampling was also stratified by group size (the number of members in the group) to better capture behavior across both smaller and larger groups. We note the following bias introduced by our sampling method: compared to all individuals who actively used groups in the past 28 days, our participants tended to be 8.7% older and were 17.5% more likely to be women.

3.2. Survey Design

The survey consisted of two sections: a section on individual differences regarding the participant’s general attitudes towards others, including disposition to trust and related concepts; and a section on trust in a specific Facebook group. Each section had four items, shown in Table 1. The order of questions was randomized within a section. Participants were asked to report the extent to which they agreed or disagreed with each statement on a five-point Likert scale.

For the section on general attitudes towards others, we measured disposition to trust through an adaptation of the generalized trust question in the World Values Survey (Survey, 2018). The original question elicits a dichotomous response, worded as: “Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?” We instead used a more granular five-point Likert scale, which has been shown to be more reliable (Miller and Mitamura, 2003). We also included measures of concepts related to disposition to trust reported in previous literature, including general social support (Barrera Jr and Ainlay, 1983; Vigoda-Gadot and Talmud, 2010; Hether et al., 2014), in-group loyalty (Van Vugt and Hart, 2004), and risk aversion (Miller and Mitamura, 2003).

To measure an individual’s level of trust in a Facebook group of which they were a member, we created a four-item scale to measure trust in groups (section two in Table 1), based on previous literature. This scale is based on the framework of ability, integrity, and benevolence by Mayer et al. (Mayer et al., 1995) and Schorman et al. (Schoorman et al., 2007), and also adapts measures from several interpersonal trust scales including Rotter Interpersonal Trust Scale (Rotter, 1971), the Specific Interpersonal Trust Scale (Johnson-George and Swap, 1982), and a newer “predisposition to trust” scale (Ashleigh et al., 2012).

In addition, to better understand what people use the group for, we also asked participants to use the taxonomy below to describe the group category:

• •

Friends & Family: e.g., close friends, extended family

• •

Education & Work: e.g., college, job, professional

• •

Interest-Based: e.g., hobby, book club, sports

• •

Identity-Based: e.g., lifestyle, health, faith, parenting

• •

Location-Based: e.g., neighborhood or local organization

• •

Other

These categories were identified in previous qualitative research, where we surveyed people who used Facebook Groups and asked them to describe a group they were part of (e.g., “my family”). In our work, participants were requested to select all categories that applied to the group they were surveyed on, and we treated each group category as a binary variable. In our sample, around 34% of the groups were tagged as interest-based groups (most common), followed by 20% friends & family groups. The first five categories capture most of the groups (covering 89%).

3.3. Data and Statistical Approaches

In addition to data from the survey, we examined properties of groups including their sizes and membership privacy policies. For each group, we also looked at an individual’s activity in the group (e.g., time spent, likes, comments, and posts made), the group’s overall activity, as well as group members’ friendships with each other.

Out of the 10,000 survey responses we received, we filtered responses based on the completeness of the survey, as well as availability of self-reported and log data. In the end, we retained 6,383 responses for our main analysis.

The main statistical techniques we used were multiple linear regression, random forests, and logistic regression. We first built a baseline model predicting trust in groups using variables capturing individual-level differences. Then, we identified five different sets of group-level features, conducted analysis on how much each set of feature improved the baseline model, and interpreted the relationship between each feature and trust separately. We next combined all features in a random forest model and compared the importance of each set of features in the combined model. Finally, we used logistic regression to predict group outcomes such as the densification of the friendship network within the group. When appropriate, we log-transformed the data (e.g., group size) and note the transformation when reporting coefficients.

4. Results

Trust in groups was measured in our survey across four dimensions: care, reliability, integrity, and risk taking. As shown in Table 2, these dimensions of trust in groups are highly correlated ($\rho\geq$ 0.54; Cronbach’s $\alpha$ = 0.86). Thus, we defined a composite “trust in groups” score as the mean of all four dimensions and report findings with respect to this composite score.

4.1. Individual Differences and Trust

We start by predicting trust in groups using individual attitudes as well as demographic information (see in Table 3), which prior literature has associated with differences in one’s disposition to trust (Taylor et al., 2007).

4.1.1. Demographics

We found that demographic factors such as the age and gender of participants capture almost no variance of trust in groups (see Model 1 in Table 3). This result partially contrasts with the prior work that found a relationship between these demographic factors and one’s disposition to trust (Taylor et al., 2007). To better understand this result, we tested a model that used demographic variables to predict participants’ disposition to trust rather than trust in groups. While we found that older people were more trusting than young people ($\beta$=$0.006$, $p$¡$.001$) and women were more trusting than men in general ($\beta$=$0.12$, $p$¡$.001$), very little variance in disposition to trust is explained by these demographic factors [$R^{2}$=$0.01$, $F(2,7174)$=$36.1$, $p$¡$.001$]. In other words, demographic characteristics explain neither an individual’s disposition to trust nor their trust in groups.

4.1.2. General attitudes towards others

How does an individual’s general attitudes towards others predict their trust in groups? Corroborating prior work, one’s general disposition to trust significantly predicts one’s trust in groups (see Model 2 in Table 3). However, other factors also play significant roles (Models 3–5 in Table 3). Notably, the individual’s perceived social support ($\beta$=$0.19$, $p$¡$.001$) and their general stated in-group loyalty ($\beta$=$0.16$, $p$¡$.001$) contributed more to the prediction of trust in group than one’s disposition to trust ($\beta$=$0.11$, $p$¡$.001$). A willingness to take risks ($\beta$=$0.05$, $p$¡$.001$) was least predictive. Altogether, these factors capture a significant amount of the variance in trust in groups (adjusted $R^{2}$=$0.14$).

4.2. Group Differences and Trust

To understand the relationship between group characteristics and trust in groups, we identified five distinct sets of group-level features (see Table 4). In this section, we measure the incremental predictive value of each of these sets of group-level features, after controlling for the individual differences discussed above. Here, we use “baseline model” to refer to a model that only includes the individual differences (Model 5 in Table 3). For each feature set, we add the features as independent variables in the multiple linear regression model to the baseline model. In each subsection, we report how much the model gains from the additional features. We validated that the coefficients of the individual differences features in the baseline do not change significantly when we include each new feature set.

4.2.1. Basic Group Properties

The first set of group-level features consisted of group size, privacy type, group tenure (how long a group has existed), the number of group admins, and its number of moderators. Adding these features to the baseline model increased the model’s adjusted $R^{2}$ by $0.08$ ($p$¡$.001$). The most significant predictor of trust was group size. Consistent with previous work on trust and group sizes (Brewer, 1991; Denters, 2002; Zelmer, 2003), people had lower trust in bigger groups ($\beta$=$-0.15$ on log scale, $p$¡$.001$).

Apart from a group’s size, a group’s privacy type can also affect perceptions of trust. On Facebook, group admins can set the group to be “public”, “closed”, or “secret”. Public groups are accessible to non-members, while closed and secret groups are only accessible to current members; closed groups differ from secret groups in whether their existence is known to non-members. We found no significant differences between closed and secret groups, so we analyzed them together as “private” groups.

Controlling for group size (public groups are 68% larger than private groups), we found that people trusted public groups less than private groups ($\beta$=$-0.07$, $p$¡$.01$), as suggested in prior work (Moser et al., 2017).

Notably, we found an interaction effect between group size and privacy type in predicting trust ($\beta$=$0.04$, $p$¡$.01$): the larger the group, the smaller the difference there is between trust in private and public groups. To see how quickly this difference between group types dissipates, we conducted a series of t-tests in which we compared the mean difference in the trust composite score between public and private groups above a certain size threshold, starting from 10 in increments of 10. These tests show significant differences between groups larger than the threshold until the threshold exceeds 150, where we no longer observe a significant difference between public and private groups ($p$¿$.05$).

Incidentally, this size threshold roughly corresponds to Dunbar’s number—the maximum number of stable social relationships a person can maintain due to limitations in cognitive resources (Dunbar, 1992). Smaller private groups provide control and exclusivity over membership, thus allowing members to foster a shared sense of identity (Moser et al., 2017). Once the group becomes too big, that shared identity might be lost, resulting in no difference between large groups that are public or private.

Figure 1 summarizes the impact of group size on trust in public and private groups, as well as the effect of an individual’s disposition to trust (we consider composite scores ¿3 to be high and ¡=3 to be low). The figure shows that having a high disposition to trust (black lines) and a group being private (solid lines) are both factors that contribute to trust in groups. But while the effect of privacy decreases with size (dashed and solid lines cross), the reverse is true for an individual’s disposition to trust. An interaction effect between group size and individual’s disposition to trust ($\beta$=$0.01$, $p$¡$.01$) shows that people with a greater disposition to trust others were less sensitive to changes in group size (visually represented by gentler slope of black lines compared to blue ones in Figure 1).

Other basic group properties also relate to trust. Longer group tenure predicts higher trust ($\beta$=$0.04$ on log scale, $p$¡$.001$), potentially because older members have more stable group relationships and are more familiar with other group members (Walther and Bunz, 2005). The number of admins also predicts higher trust ($\beta$=$0.10$ on log scale, $p$¡$.001$). This finding is consistent with previous work that found that groups with more admins tended to survive longer than groups with fewer admins (Kraut and Fiore, 2014). The number of moderators is a much weaker predictor of group trust.

4.2.2. Group category

As previously described, participants in our survey labeled groups as belonging to one or more of six categories. Including group category as multiple binary variables to the baseline model significantly improved trust predictions ($p$¡$.001$), increasing the model’s adjusted $R^{2}$ by $0.05$. To illustrate differences in trust across these categories, we also conducted an ANOVA and plotted the average trust in groups by category in Figure 2. Groups marked as “other” were excluded from this analysis. Post-hoc Tukey tests showed that people trust friends & family groups the most, followed by identity-based and education & work groups ($p$¡$.001$). They trust interest- and location-based groups least ($p$¡$.001$).

Why does trust differ by group category? For friends & family groups, high trust is a strong sign of bonding social capital (Putnam, 2000). Identity-based groups (e.g., parenting groups) and education & work groups elicit trust by establishing a shared identity among group members (Moser et al., 2017). Finally, interest- and location-based groups may represent less bonding and more bridging social capital (Granovetter, 1985), especially for information sharing. People use these groups more as places to transact and exchange (both physical goods and information) than as places to build relationships (Granovetter, 1985). By comparing groups across different categories, we can develop a more holistic understanding of trust across different types of social groups that also draws on insights from previously isolated studies (Moser et al., 2017; Holtz et al., 2017).

4.2.3. Activity

Here, we studied both a survey participant’s activity in a group as well as the overall group activity across all members. Measures of activity include time spent in the group and the number of actions (posts, likes, or comments) taken in the group, averaged across the 28 days preceding the survey. In the case of public groups, activity also included contributions from nonmembers. An individual’s overall site engagement was not predictive of trust, and thus was excluded from our analyses. Including activity features (time spent, group activity, and participant in group activity) to the baseline model improves its adjusted $R^{2}$ by $0.04$ ($p$¡$.001$).

As many activity features are correlated, we report coefficients when the feature is independently added to the baseline model. Time spent in the group, both by the individual ($\beta$=$0.04$, $p$¡$.001$) and by other group members ($\beta$=$0.05$, $p$¡$.001$) independently predicts higher trust in groups. Overall, the number of posts per member ($\beta$=$0.07$, $p$¡$.001$), and the number of likes and comments per post ($\beta$=$0.07$, $p$¡$.001$) were also both independently associated with higher trust. However, the number of comments and likes a participant made in a group was associated with higher trust ($\beta$=$0.10$ on log scale, $p$¡$.001$), but not the number of posts the participant wrote.

Why is this the case? Posting in a group may be influenced by a variety of factors other than trust (e.g., self-esteem (Forest and Wood, 2012)). In contrast, likes and comments are forms of directed communication that people use to maintain relationships with others (Ellison et al., 2014) and may therefore be more conducive to building trust.

4.2.4. Homogeneity and homophily

Trust may also be influenced by how similar people in a group are to each other (homogeneity), and how similar an individual is to others in the group (homophily).

For each group, we measured age and gender diversity by computing the gender entropy of the group’s members and the standard deviation of their ages. To measure homophily, we constructed a simple distance measure based on the approach of (Abrahao et al., 2017). If the participant had the same gender with the majority of the group members, we coded the gender distance as 0, otherwise 1. If the participant’s age was within 5 years of the average age of the group, we coded the age distance as 0, otherwise 1. The total distance from average group members was calculated as the $L_{1}$ distance, i.e., the sum of gender and age distance $\in(0,1,2)$. As different types of groups may have different demographic compositions, we controlled for group category in this analysis.

Adding homogeneity and homophily features to the baseline model results in a small improvement (increased adjusted $R^{2}$ by less than $0.01$, $p$¡$.001$). Nonetheless, we found that both gender ($\beta$=$0.04$, $p$¡$.001$) and age homogeneity ($\beta$=$0.04$, $p$¡$.001$) were associated with higher trust.

Surprisingly, homophily, measured as described above, was not predictive of trust in groups. This contrasts with findings in previous work on trust and homophily in dyadic exchange, which found that trust increases with gender and age homophily (Abrahao et al., 2017; Ahmad et al., 2011). While we only studied age and gender homophily here, future work may consider other forms of homophily (e.g., with respect to interests, location, or socio-economic status) or other measures of homophily, especially in a group rather than dyadic context.

4.2.5. Network structure

To understand how network structure mediates trust, we calculated the following network features for each group:

(1) network density: the number of friendships in the entire group friendship graph divided by the number of possible combinations;

(2) average clustering coefficient: the average local clustering coefficient in the group membership graph, which measures what proportion of an individual’s friends also know one another;

(3) participant degree centrality: the number of friends a participant has in the group, normalized by group size;

(4) $k$-core existence: a measure of how a participant’s friends in the group are connected with each other, calculated as whether a $k$-core component (Ugander et al., 2012) exists for participant’s friendship graph in the group (we found that $k$=$5$ resulted in the greatest model improvement);

(5) average mutual friend count: the mean number of mutual friends between participant and group members.

Figure 3 illustrates how several group networks in our sample differ along these network features. For example, Group A has higher network density and higher average clustering coefficient than group B. Groups C and D differ in the participant’s degree centrality. Group D contains a $5$-core, but E does not.

These network features, when added to the baseline model, improves its adjusted $R^{2}$ by $0.10$ ($p$¡$.001$). Each feature was positively associated with trust in groups ($p$¡$.001$), though we note that these network features correlate highly with one another. Considering these features separately, the average clustering coefficient was most predictive ($\beta$=$1.08$, $p$¡$.001$), followed by group density ($\beta$=$0.93$, $p$¡$.001$) and the participant’s degree centrality ($\beta$=$0.84$, $p$¡$.001$).

4.3. Predicting Trust in Groups

Thus far, we have shown how various sets of group characteristics separately contribute to trust, after controlling for individual characteristics. Here, we examine how these features can together predict composite trust in groups.

A random forest model that uses all feature sets (in both Table 3 and Table 4) reached a performance of out-of-sample adjusted $R^{2}$ of $0.26$ and a mean-squared error (MSE) of $0.53$. We obtained similar performance using multiple linear regression.

To understand the relative importance of the different feature sets, we ranked all features by how much a random permutation of their values increased the model’s MSE. These values are shown in Figure 4. We find that network features are most important, followed by an individual’s general attitude towards others. Least important were demographic features. Overall, this result suggests that both individual and group characteristics are important in predicting trust in groups.

4.3.1. Predicting trust using only observational data

As we demonstrate relatively robust performance in predicting trust in groups, one might consider using such predictions to make better group or community recommendations. However, our model uses survey responses about individual differences, including disposition to trust and related concepts, to make predictions about trust in a specific group. In practical settings, it may not be feasible to administer the survey questions on individual differences to all users. This motivates the question of how well our modeling approach works in the absence of the individual differences survey features. Excluding these features, our best model obtained an adjusted $R^{2}$ of $0.15$ and $MSE$ of $0.59$. In this model, network structure features were again most important, but instead followed by group activity features.

4.4. Group Outcomes

Theoretical accounts of trust emphasize the impact of trust on community outcomes, attributing trust to prosperity (Fukuyama, 1995), among other things. Here, we analyze how trust relates to three different group outcomes:

(1) the percentage change in group size,

(2) the percentage change in new tie formation (the number of new ties divided by the number of pre-existing ties) among other members of the group, and

(3) the percentage change in new tie formation by the survey participant in the group.

All three measures were calculated by comparing the state of the group on the day of the survey to that 28 days after. As these changes tend to be small, with a median change of about 1%, we instead predict whether each measure would increase by more than 1%.

Figure 5 shows the percentage of groups that exhibited an increase by more than 1% in each of the group outcomes listed above. Using logistic regression and controlling for basic group properties such as group size, we found that higher trust was associated with a lower likelihood of a group increasing in size (odds ratio -0.87, $p$¡$.001$); and a higher likelihood that the survey participant would form more new friendships in the group (odds ratio 1.29, $p$¡$.001$). Trust in a group was not predictive of the likelihood of other group members forming friendships in the group.

These results suggest a tension between trust and growth for online groups. Our findings are consistent with previous work on online communities that found that “cliquishness” (or high triangle density) makes a community less attractive to join and less likely to grow in size (Backstrom et al., 2006). While membership growth is an important indicator of success for online groups (Kraut and Fiore, 2014), trust, partially elicited by small groups and exclusive membership (Moser et al., 2017), can limit group expansion (but nonetheless encourages individuals to make new connections within the group). Future work can examine the relationship between trust and group longevity, as well as other interaction dynamics such as forming sub-communities within the group.

5. Discussion

In this paper, we present a framework for predicting an individual’s trust in one of their social groups on Facebook. Combining a large and diverse survey with behavioral and demographic data, we show that both individual characteristics and group characteristics contribute substantially to trust. We are able to explain a significant portion of an individual’s trust in groups ($R^{2}$=$0.26$) as well as show how trust relates to outcomes such as membership growth and the formation of new within-group friendships.

This work builds on many previous studies of trust in groups by showing how features previously studied in isolation may interact with each other and how important these features are relative to each other. Beyond confirming that both an individual’s general disposition to trust others (Ferguson and Peterson, 2015) and a group’s size (Brewer, 1991) affect that individual’s trust in a group, we further show that group size matters less to individuals with a greater disposition to trust, and that an individual’s feelings of receiving social support from others in general is actually more predictive of trust in groups than their general disposition to trust. Apart from demonstrating that people do trust smaller, more private groups, we show that among groups with more than 150 members, the effect of exclusive membership decreases. Where previous work has suggested a relation between group connectivity and trust (Coleman, 1988; Yuki et al., 2005), we also demonstrate that network measures such as the average clustering coefficient in a group are among the strongest predictors of trust in a group. Our findings on how directed communication such as likes and comments contributes to group trust corroborate similar observations in qualitative studies (Moser et al., 2017).

Nonetheless, several null results suggest areas for future exploration. While prior work suggests trust differs with sociodemographic factors (Center, 2007), we found that age and gender explain close to zero variance in one’s trust in groups. Future work may consider exploring other factors such as geography or socioeconomic status. Cultural differences may also play a significant role in trust: prior work found that an indirect relationship between two people was more likely to increase trust for Japanese than Americans (Yuki et al., 2005), suggesting that network structure may be more predictive of trust among the former. Though we found that gender- and age-homogenous groups were more trusted, we also found no evidence that gender or age homophily predicts trust in groups, in contrast to previous literature suggesting that relationships between similar individuals tend to be more trusting (Abrahao et al., 2017; Ahmad et al., 2011; McPherson et al., 2001). Understanding the extent to which these findings apply to specific situations — moms’ buy-and-sell groups on Facebook are known to garner trust (Moser et al., 2017) — remains future work.

Future work may also involve investigating other potential correlates of trust such as psychological safety (Edmondson et al., 2004) and belonging (Zhao et al., 2012), as well as other outcomes of trust on online groups. For example, high trust may lead to a greater willingness to attend an event, share (or believe) information originating from within the group, or donate to a cause.

5.1. Design Implications

The work reported here has several potential implications for the design of online communities.

We showed that certain types of actions (e.g., commenting and liking) are more positively associated with trust than others (e.g., posting). This adds nuance to previous findings that people have greater trust in communities in which they are more active  (Cartwright and Zander, 1953). As such, platforms could prioritize facilitating directed interactions among group members, for example, expanding features to support polling, brainstorming, and collective planning. At the same time, these findings may also inform the design of content recommendation systems. If these findings indicate that directed communication is a key signal of trust, then incorporating such signals of directed communication may better ensure that people see more content from communities that they trust more.

Consistent with prior work  (Kraut and Fiore, 2014), we found that trust grows with the number of group admins and decreases with group size. As online communities grow, it may be beneficial for platforms to encourage groups to recruit additional admins to maintain existing levels of trust.

Further, network properties of online communities such as the average clustering coefficient are strong predictors of trust. Adding members that increase the average clustering of the group may be beneficial both to new members and to the group as a whole.

Given that trust in a group correlates with behavioral signals, with additional research, platforms may also be able to provide a rough indicator of trust in groups and how it may be changing over time.

Last, our findings suggest alternative strategies for recommending groups to individuals. For instance, recommending smaller, less popular groups may not only increase the diversity of group recommendations, but also lead to greater trust and user satisfaction.

5.2. Limitations

Our analysis is limited to groups on Facebook. Understanding how trust differs in communities with different policies on anonymity (e.g., Reddit or Nextdoor) or that have different feedback mechanisms remains an important area for future exploration. Anonymity may increase trust by making it easier for vulnerable populations to talk about sensitive issues, but also have a disinhibiting effect and increase harassment and thus reduce trust (Kiesler et al., 1984); indicators of reputation or popularity such as up-votes and down-votes may also influence trust, especially in the absence of other social signals (Resnick and Zeckhauser, 2002). Still, many group properties (e.g., group size) that we examined apply to groups in general; the interactions (e.g., posting or liking) that we looked at are also common on other social media platforms. Along with the large number and diversity of groups we surveyed, we expect that many of our findings will generalize to other online communities111Code to reproduce our analysis is available at https://github.com/facebookresearch/trust-in-groups. While we controlled for individual differences such as demographics and an individual’s general attitudes towards others, understanding differences that may arise in offline groups and with regards to other factors such as location remains future work. Also, individuals may choose to join groups based on other unobserved differences (e.g., word-of-mouth). Finally, our methodology is based on correlations between variables and cannot directly suggest causation. Most significantly, it is possible that individuals who have different propensities to trust tend to join entirely different groups, explaining some of our observed differences. Similar limitations apply to the group outcomes analysis. While greater trust may lead one to connect to other members of a group, it may also arise from making these connections.

6. Conclusion

Groups play a significant role in an individual’s social experiences and interactions. Trust, which predicts numerous positive outcomes for a group and its members, is core to a group’s proper functioning. In this paper, we presented a framework for predicting an individual’s trust in a social group, and identified characteristics of both the individual and the group that help predict the individual’s trust in the group. This work can contribute to future research and design decisions that better support trust in online communities and foster long-term meaningful interactions online and offline.

Acknowledgements

We would like to thank Steve Carter, Nick Brown, Ann Hsieh, Lada Adamic, Moira Burke, Israel Nir, Alex Dow, and our reviewers for their help and feedback.