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Rich Get Richer Offline and Online

A critical aspect of psychological well-being is being socially connected. It is a serious topic, which is why in the article I read, researchers wanted to investigate the relationship between student loneliness and their patterns of use on social networking sites, such as Facebook.

Interactions between media use and psychological well-being are known to have two major paradigms, which are the deficiency paradigm and the global use paradigm. The deficiency paradigm is the use of media as a replacement for unsatisfactory face-to-face interactions while the global use paradigm is where media use is seen to be the same as face-to-face interactions. And with these two paradigms, it’s predicted that for the deficiency paradigm, the loneliest students will attempt to compensate for their lack of face-to-face interactions through online interactions and the global use paradigm predicts your offline ability to connect with other reflects your online ability and that the two environments are the same. And with these two paradigms, we can see that the global use paradigms would thus, reflect the richer get richer phenomenon because if online and in person social interactions are the same, people who do well in face-to-face interactions will also do well in online interactions and thus, be able to connect with more people than someone who struggles in person.

To prove which paradigm is more accurate, a study was thus conducted on a university. A convenience sample of 124 undergraduate students, ages 18 to 24, at a large university in California were the participants of the research. They were all given a questionnaire to assess demographic variables, questions related to connectivity, participation in social network services like Facebook, and the UCLA Loneliness scale. The primary focus was on the relationship between subjective loneliness and the measure of offline and online social connectivity.

Graph above shows the average number of Facebook friends people of Low Loneliness (LL), Medium Loneliness (ML) and High Loneliness (HL) have.
Image above shows the negative correlation between the UCLA Loneliness score and the number of Confidants and FB friends someone has.

The mean score for the UCLA Loneliness scale was 37.78, which falls into the medium range of loneliness and 95% of participants reported being active participants on Facebook. Loneliness however didn’t affect the likelihood of participation on Facebook. And as seen by the arrows above, in fact the scores of the UCLA Loneliness scale and number of confidants and FB friends were negatively correlated. These findings suggest that many of the obstacles of feeling connected in everyday life exists in online social networking sites as well. So thus, given that the findings that online and offline connectivity are similar for people, it turns out that the global use paradigm is more accurate. So thus, more lonely people do not find more friends online as a way of compensating for their real-world experiences.

This connects to the course because as learned in lecture in the richer get richer lesson, new nodes are more likely to link to nodes that already have a high degree. This can be seen with people who have large social networks in person. Because if they have large social networks in person, this will usually lead to a larger social network on social media sites because usually people you know in person are the ones you add online, which is why the global use paradigm is more accurate than the deficiency paradigm. If you are able to connect with people easily in person, you will be able to do the same online, which thus, shows an example of the richer getting richer in this research article.

References: http://www.jlampl.net/OnlineLonliness.pdf

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Optimizing Social Networks

Social media is common and widespread. It’s a useful tool to connect with others and to expand your social network by reaching out to meet new people, whether it be for job recommendations, friendships or more. When finding new people to connect with, we either search for them or we ask others to reach out to the person they may know. However, based on my research, this isn’t necessarily the best way to connect with someone. This method fails to include the strength of a social tie between two people and the asymmetry of social relationships, which leaves it less efficient than an algorithm that would include those two criteria. That’s why, this article has theorized and shown that adding directed weighted edges that store an influence one person has on another to social network graphs will cover social tie strength and asymmetry, and thus make the algorithm more efficient in finding someone to connect with.

An influence is the power one person has over someone, which could lead to them being more likely to follow their instructions. For example, an influence a person A has over a person B, is said to be the amount of investments B makes on A. A and B may be two Twitter users and if B interacts with A often, it’s said that A has a strong influence on B. And if A doesn’t interact with B back, B doesn’t have a strong influence on A. This shows the asymmetry in the social tie here and this has an affect on whether someone can reach their target person. Below is an example of a graph with weighted edges storing influence added to it.

Nodes can be thought of as people. The right graph shows the influence one person has on another.

With this idea, the algorithm to find the strongest path, which includes weighing influence, ends up being better than the shortest path algorithm when it comes to connecting with someone. Twitter was used as an example in the article I read where they used both the strongest and shortest path algorithms to determine which one was better to go from a source person to a target. And it turned out, the strongest path is better for 68% of searches in Twitter than the shortest path. This is because if B wants A to introduce them to someone A knows, this might not work if B has no influence over A because A might not necessary agree to introduce them. But if B did have a strong influence on A, then A would be more likely to introduce them. Which means that influence plays a huge role on whether a person will reach their target person.

Overall, as learned in class, social networks are very important and the article I found taught us that influence plays a strong role in the way we meet people through networking. In a social network graph, it’s beneficial to have a weight on the edges to store how much influence one person has on another because this increases the chance of you being able to meet someone through them. And in this day and age, it’s important to have connections, so with the strongest path algorithm this idea of influence can assist others in expanding their social networks.

References: https://homes.cs.washington.edu/~jheer/files/snakdd.pdf