Gambling social network

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Joshua D. Psychological Medicine. Psychology of Addictive Behaviors.

Gambling social network gambling high

Pathological gamblers differ from non-pathological gamblers in the number of gamblers, smokers, and drinkers in their social networks. Homophily within the networks also indicates that gamblers tend to be closer with other gamblers. This homophily may serve to reinforce addictive behaviors, and may suggest avenues for future study or intervention.

Social factors contribute to the initiation and maintenance of gambling behavior. For example, the most frequent reason for gambling among older adults reported was to socialize with friends 1. In a college-aged sample, social factors were the third most cited motivation to gamble 2. Recreational gamblers and pathological gamblers PGs who were introduced to gambling in early life were at the greatest risk of developing gambling problems 5.

Further, as adolescents age and their gambling involvement increases, they spent more time with their gambling friends, resulting in fewer close relationships with non-gambling friends 6 , which may result in a pernicious cycle of a social network that reinforces gambling, which in turn results in spending more time with gambling friends. Social factors, as well as perceptions of social norms, are also implicated during gambling. For example, participants who believe that others are gambling and winning, play for longer periods resulting in greater losses 7.

In the presence of onlookers, people place smaller bets 8 , suggesting that social factors can have a considerable impact on gambling play. When students perceive that important others approve of gambling, they gamble more frequently 9. The current study utilized an established method that has only recently been applied to gambling and other addictive behaviors.

Social network analysis SNA is an innovative technique for understanding group prevalence and structure. In a sociocentric network analysis, by contrast, information is gathered from each person, about each person, in a relatively closed network.

A frequent focus of SNA studies is homophily, or the tendency of individuals who are similar in their beliefs, attitudes, and behaviors to be more frequently and more closely linked in social networks than those who are dissimilar In his classic housing study, Festinger 12 found evidence of homophily based on propinquity, the tendency of people who live close together to be more connected. Social network analysis is also used to examine the structural characteristics of social networks.

One structural characteristic that may affect addictive behavior is network density, which reflects how connected the members of a network are to each other. Dense networks make it easier for egos to observe and to replicate the behaviors of alters in their network Within the DSM-IV, pathological gambling PG is categorized as an impulse control disorder defined by symptoms including loss of control of gambling, preoccupation with gambling, and persistence despite negative consequences The DSM-5 will most likely categorize PG under Substance Use and Addictive Disorders, reduce the diagnostic threshold from 5 to 4 symptoms and eliminate the criterion of illegal activities SNA has been successfully utilized to study substance use and abuse.

Homophily has been observed in the addiction domain. For example, drinkers prefer friends with the same drinking and smoking behavior 17 , We therefore posit that individuals who gamble, smoke and drink, will be more frequently and more closely connected to others who gamble, smoke and drink, respectively.

Peer group substance use has also been examined in several studies utilizing sociocentric SNA applied to samples of middle and high school students. Liaisons have been found to smoke more than others, but are less affected by the prevalence of smoking in their networks Surprisingly, there is no effect of network position on alcohol use, but alcohol use is related to the proportion of network peers who use alcohol. As the prevalence of alcohol and marijuana use increases in peer networks, so does the frequency of an individual engaging in that behavior The primary aim of the present study was to apply SNA to PG for the first time, investigating the role of social networks in PG, in a comparison of recreational gamblers and problem gamblers.

We hypothesized that, compared to nonpathological gamblers NPGs , PGs would have social networks that were denser with gamblers and also differed structurally. However, in the absence of previous studies, no a priori hypotheses were made for specific structural indices. We also hypothesized that PGs would engage in all of these behaviors more often than NPGs with their network members. As friends have been found to be a primary reason to gamble for older adults, we also hypothesize that they will have significant impact on gambling, smoking, and drinking behavior.

All participants were recruited through advertisements in newspapers and buses as well as word-of-mouth. Exclusion criteria were gambling less than weekly, currently living with another participant, inability to use a computer, self-reported symptoms of psychosis, or age greater than 65 years. Participants were an average age of Most participants were African American The amount of structural information gained about a network increases as the number of alters increases, but begins to plateau around 25 alters, with 35 alters providing virtually identical information as 45 alters Participants did not report difficulty listing the 30 alters, although tests of order effects revealed some significant differences in gambling or substance use between later- and earlier-named alters reported below.

Participants indicated the sex and race of each alter, how long he or she knew each alter, how often he or she spent time with each alter, how close they were, whether they ever lived together, and whether they ever were in a romantic relationship with one another. Participants also indicated how frequently each alter gambled, smoked, and drank, and how often the participant gambled, smoked, and drank with each alter.

Each of these behaviors was assessed on a 6-point Likert frequency scale that included the following levels: Participants additionally answered questions about the relationships among the alters. Each alter pairing was rated on a scale ranging from very close 5 to they have never met 1. Assessment was conducted using EgoNet, a program designed for the collection of egocentric social network data Social networks were structurally characterized using the validated SNA indices of network density and betweenness centrality.

Network density is the proportion of the number of actual connections relative to the number of possible connections in a network. Dense networks have many strong connections between members whereas a less dense network has fewer and weaker connections. Betweenness centrality is the degree to which the shortest paths between any pair of people in the network pass through a particular alter A Jonckheere-Tepstra test 29 was used to analyze differences in gambling, smoking and drinking frequency between the social networks of PGs and NPGs, as well as the frequency of joint engagement in these behaviors by ego and alter together.

These use median values, with lower numbers representing higher frequencies. We also used a Mann-Whitney U test to examine differences in network density. Data analysis was conducted on SPSS All non-dichotomized independent variables were grand mean centered. See Table 1 for full descriptive statistics.

As revealed in Table 2 , the networks of PGs had frequency distributions that were more weighted to frequent engagement in all three behaviors. All values are percentages. Figure 1 presents examples of PG and NPG networks, selected to be maximally illustrative of the effects in question. A line between two nodes represents a connection between alters, and darker and larger nodes represent more frequent gambling, ranging from black daily to white not in the past year.

In each case, the networks reveal the greater occurrence of gamblers, smokers, and drinkers for the PG participant; in contrast, the NPG participant exhibits a network in which the addictive behaviors are restricted to more distinct subgroups of associates. Structural social networks of gambling, alcohol use, and tobacco use in two illustrative participants.

The participant is not shown in the graphs. Darker colors and larger nodes reflect more frequent gambling, drinking, or smoking behavior. In each case, the networks reveal the significantly greater occurrence of gamblers, smokers, and drinkers for the PG participant; in contrast, the NPG participant exhibits a network in which the addictive behaviors are restricted to more distinct subgroups of associates.

In general, the effects appeared stronger among friends as opposed to all network members, as floor effects on frequency were attenuated. Similarly, when using dichotomized behaviors less than once a month vs. We also tested the relationship between subjective closeness and gambling severity. The relationship between closeness and frequency of gambling, smoking and drinking is further borne out by associations between order of identification and all three addictive behaviors.

To our knowledge, the current study constitutes the first formal social network analysis of pathological and recreational gamblers. This is a particularly promising methodology for gambling studies, both insofar as SNA has made significant strides in other addictive behaviors 19 , 21 , and as social factors are known to contribute substantially to PG 1 , 2.

We also found that PGs gambled, smoked and drank more frequently with members of their networks than did NPGs. Consistent with our hypotheses, PGs were found to gamble, smoke and drink alcohol significantly more often with their friends than NPGs did. There are two prominent theories on why social affiliates engage in similar behaviors: Research suggests that socialization is associated with closed, tight networks e. As the current study is cross-sectional, it cannot differentially support either of the two theories, but it clearly represents a methodology that, applied across time, could clarify whether individuals with PG seek out similarly affected people or whether social groups directly confer risk for developing PG.

Participants felt significantly closer to alters who gambled, smoked and drank more frequently. Surprisingly, we found that this effect was virtually identical for the PG and NPG groups in their subjective feelings of closeness to the gamblers and smokers in their networks. Furthermore, when comparing the networks of PGs and NPGs, there were no differences in subjective closeness for friends and the entire network. These results suggest that PG status does not directly affect closeness and that closeness may be defined by several other factors besides mutual interests.

Alters who gambled were more connected to others who gamble, and those who did not gamble were more connected to others who did not gamble. Consistent with these results pertaining to gambling, homophily is also found in drug using networks 16 , This suggests that the networks of gamblers are similar to those of substance abusers.

The absence of significant differential homophily and network density may have been due to issues of range restriction arising from the entire sample being comprised of gamblers. In a study examining heroin injectors and non-injectors, the authors found that although injectors had more friends and a larger network size, there was not a significant difference in network density between the two groups Similarly, the main differences between these networks in our data were compositional, not structural.

That is, taken together, the most salient social network factors observed for PG participants were significantly more gamblers in the network, more frequent gambling among those gamblers, and significantly greater joint gambling with network members. The lack of difference in density, which reflects closeness among alters and not between ego and any alters, is independent of the low social support that is associated with greater gambling severity Strengths of this study include the systematic application of an SNA approach to PG and a well characterized sample with considerable diversity.

However, limitations include that the participants reported the behavior of others in their network, possibly resulting in a false consensus effect, an inherent limitation of egocentric SNA wherein participants project their own behavior onto others Future research would benefit from utilizing a sociocentric network design and a longitudinal design that addresses the causal role of social influence and selection on addictive behaviors.

Another limitation of this study was its relatively modest sample size, which may reduce the generalizability of the findings. We also cannot eliminate the possibility of overlapping networks, as alters were kept anonymous. Future research will be needed to establish whether the correlational effects reported here are attributable to gambling problems per se or to gambling frequency. Finally, the current study included higher proportions of African American and low-income individuals than is reflective of the broader US population, likely due to these demographic characteristics being more prevalent in the recruitment catchment area.

These caveats notwithstanding, the current study advances understanding of the role of the social network in addictive behavior by providing the first formal SNA of pathological gambling. Pathological gamblers had more gamblers, smokers and drinkers in their networks in general and more individuals with whom they personally gambled, smoked, and drank alcohol.

These compositional differences may provide important insights into the causes and maintaining factors in PG and, ultimately, may also be leveraged to enhance treatment. Declaration of interest: Miller and Dr. Campbell receive funding from the Institute for Research on Gambling Disorders. None of these sources constitutes a conflict of interest with this study. National Center for Biotechnology Information , U.

Author manuscript; available in PMC Mar 1. Matthew K. Meisel , 1 Allan D. Miller , 1 W. Keith Campbell , 1 and Adam S. Goodie 1. Allan D. Joshua D. Keith Campbell. Adam S. Author information Copyright and License information Disclaimer. Corresponding author: Copyright notice. The publisher's final edited version of this article is available at Addiction. See other articles in PMC that cite the published article.

Abstract Aims To apply social network analysis SNA to investigate whether frequency and severity of gambling problems were associated with different network characteristics among friends, family, and co-workers. Design Egocentric social network analysis was used to formally characterize the relationships between social network characteristics and gambling pathology.

Gaming capitals are a way for developers to increase replay value provides extended play time, and players get more value from the game. Events timed to real world: Popular games such as Dragon City and Wild Ones require users to wait a certain time period before their "energy bars" replenish. Without energy, they are unable to conduct any form of action. Gamers are forced to wait and return after their energy replenishes to continue playing. Social network games frequently monetize based on virtual good transactions, but other games are emerging that utilize newer economic models.

An example of is Empire Avenue , a virtual stock exchange where players buy and sell shares of each other's social network worth. In Empire Avenue , a player's worth is linked to his or her social media influence and activity, as well as that of the other players he or she has invested virtual currency in.

This game design promotes social media interaction as a means to attaining higher value in Empire Avenue market rankings. Gamers will be able to purchase in game items like power-ups, avatar accessories, or decorative items users purchase within the game itself. This is realized by monetize products that don't technically exist. Designers optimize user experience through additional gameplay, missions, and quests, without having to worry about overhead or unused stock.

The following are common ways of advertising in social network games: However, because social games generate so many page views, they are the biggest part of advertising revenue for the social gaming industry. Videos are the ad format with the most revenue per view. According to studies, video ads result in highest brand recall thus a good return on investment for advertisers. Video ads are shown either in in-game interstitials e. A brand or product will be injected in a game in some way.

Due to the variety of ways in which product placement can be accomplished in any media, and because the category is nascent, this category is not standardized at all, but some examples include branded in-game goods or even in-game quests. For example, in a game where you run a restaurant, you might be asked to collect ingredients to make a Starbucks Frappuccino, and receive in-game rewards for doing so. Another form of advertising that is prevalent in many social games are lead generation offers.

In this form of advertising, companies, usually from different industries, aim to convince players to sign up for their goods or services and in exchange, players will receive virtual gifts or advance forward in the game as a reward. Applications that are built once, then individualized and licensed again and again. Developer can create a quality app focused on fun while leaving the edges of the game open for branding. This allows developers to market their game to companies that can find new and interesting ways to bond with, expand, or sell to their audience.

Large established corporations are using social gaming to build brand awareness and engagement. The gameplay is divided evenly between two main elements, finding hidden object and large assortment of animals, and also includes simulation for players to build their own nature preserve.

Players are expected to work with friends to collect the necessary materials to grow their habitat, while the hidden object element set players to compete for the highest score in their social setting. Some large established video games developers are acquiring small operators to capitalize on the social gaming industry. Cow Clicker , created by Ian Bogost , was developed to highlight social games' most exploitative and abusive aspects.

The game requires users to click on a picture of a cow every 6 hours to earn points. It also prompts users to encourage friends to join in to help them gain more points. Cow Clicker was clearly designed to ridicule other social media games such as FarmVille , yet fifty-six thousand users played it at its peak.

The community also evolved and spawned similar games, garnered critical reviews and even gained a strategy guide. In a study by Bitdefender , it was shown that social games increase spam and phishing by 50 percent in social media platforms. This is made possible through hackers creating fake profiles and relying on bots to send spam messages to other users via social gaming applications. Many of these users who receive the messages willingly add the spammers' fake profiles into their circle of friends to depend on them for additional gaming support.

In doing so, several users have become more prone to being victims of data, identity theft, account hijacking, and other issues. The spammer's action here, however, does not constitute as abuse since it is typically the user who adds the spammer on their end. As such, the spammer's account cannot be suspended by a social network. Social networking gamers are also susceptible to unwanted charges. For instance, some of these games offer virtual currency if the player fills out a survey.

After completing the survey, users are asked to type down their phone number, then wait for a text message that will give them a PIN to enter into a site and will finally give them their results. By entering the PIN into the site, they are subscribed to some service—such as ones that provide horoscope forecasts—are charged for it, and may not be aware of it unless they have carefully read the fine print.

Some critics have also claimed that social networking games have caused the numbers of fake profiles to rise. Creating a fake profile can be advantageous if the game, for example, offers rewards whenever a user introduces the game to their friends. By inviting the fake profile to play the game, the user can trick the games' point-based system into thinking that they are actually helping the game gain popularity and in return, they may receive rewards from the game.

Social networking sites such as Facebook eliminates fake profiles if and only if these profiles are reported by other users. One of the more popular genres to social games are those that imitate gambling activities which are free to play and easily accessible through a social network. However, the similarity these games have with gambling has also created a debate about whether or not social games need to be regulated.

Several policymakers from various countries—Australia, Belgium, Spain, and the United Kingdom—have shown concern about the potential and negative impact these games could cause. From Wikipedia, the free encyclopedia. Redirected from Social network game. Part of a series on: Video games Platforms. Arcade video games Best-selling video games Best-selling video game franchises Highest-grossing video game franchises Most-played video games by player count Most-played mobile games by player count Highest-grossing arcade games Video games considered among the best Game of the Year awards Video games notable for negative reception.

This article may contain indiscriminate , excessive , or irrelevant examples. Please improve the article by adding more descriptive text and removing less pertinent examples. See Wikipedia's guide to writing better articles for further suggestions. October Retrieved 11 October Retrieved 24 March Archived from the original on 24 March Happy Farm ".

The 15 Most Influential Games of the Decade. Retrieved 10 September Retrieved 29 June Retrieved 10 June Game On: Principles and patterns of social games: Where's the difference compared to other games? Archived from the original on Retrieved Retrieved 22 March Retrieved March 31,

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Gambling and Social Media May 18, - Social media and gambling share the same addictive features. The research aimed to map the social networks of low- and moderate-risk gamblers, in terms of their gambling and other potentially risky behaviours, such as. Oct 25, - A social networking site that is focused on gambling, allows individuals who all have a common interest to join up and converse about their.

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