Re: Media Player For_The Social Network
Gifford Brickley
Market leader Facebook was the first social network to surpass one billion registered accounts and currently sits at more than three billion monthly active users. Meta Platforms owns four of the biggest social media platforms, all with more than one billion monthly active users each: Facebook (core platform), WhatsApp, Facebook Messenger, and Instagram. In the third quarter of 2023, Facebook reported around four billion monthly core Family product users.
The leading social networks are usually available in multiple languages and enable users to connect with friends or people across geographical, political, or economic borders. In 2022, Social networking sites are estimated to reach 3.96 billion users and these figures are still expected to grow as mobile device usage and mobile social networks increasingly gain traction in previously underserved markets.
A procedure is described for finding sets of key players in a social network. A key assumption is that the optimal selection of key players depends on what they are needed for. Accordingly, two generic goals are articulated, called KPP-POS and KPP-NEG. KPP-POS is defined as the identification of key players for the purpose of optimally diffusing something through the network by using the key players as seeds. KPP-NEG is defined as the identification of key players for the purpose of disrupting or fragmenting the network by removing the key nodes. It is found that off-the-shelf centrality measures are not optimal for solving either generic problem, and therefore new measures are presented.
Stephen P. Borgatti is Professor of Organization Studies at the Carroll School of Management, Boston College. His research is focused on social networks, social cognition and knowledge management. He is also interested in the application of social network analysis to the solution of managerial problems.
Social media has simplified sports marketing. Many sports marketers leverage social channels to promote sports-related events and activities. And many organizations are getting fans involved in creating original user-generated content (UGC) to build deeper connections.
Overall, sports are increasing in popularity. One of the reasons is the rise of social media, the growing importance of short-form content, and the need for sports teams and leagues to start gathering and sharing all-access content.
TV was the only way to broadcast live sports or events visually for many decades. Now, sports organizations and media outlets can leverage social media on mobile screens to share live events and real-time games with audiences.
Social media also helps people stay connected with their favorite sports teams and athletes. For example, during the early days of the COVID-19 pandemic, many competitions were suspended. But social media allowed organizations to create fresh content, keep fans updated about their favorite players and coaches, and engage their audiences.
In addition to being a news medium, social media helps organizations boost fan engagement and partner revenue opportunities. This happens through targeted social media sports marketing campaigns using digital content.
At the moment, TV broadcast revenue, sponsorships and ticket sales make up most of any team or league revenue. Social media provides a new opportunity to expand and grow revenue from those same channels. It also opens up new opportunities.
Social media have influenced sports, particularly in marketing and communications. Many sports marketers now capitalize on social media to promote campaigns, events, teams and sports activities. Also, social media is changing traditional journalism: Sports media outlets now share content with mass audiences via social media platforms daily.
With social media, athletes are no longer just athletes but also commercial brands. Athletes benefit from having social media to promote themselves, build a personal brand profile, and connect with their fans year round. Moreover, athletes can enjoy job opportunities and economic benefits based on their reputation on social media.
Artificial intelligence has opened up new ways to increase sponsor revenue for leagues and clubs everywhere.
A must-read for heads of commercial and corporate partnerships. (no registration required).
Churn prediction models are a commonly used tool in industries operating in competitive markets where customer turnover is high. From an organizational perspective, retaining an existing customer is the economical choice in contrast to attracting new customers, where, in addition, long-term customers are less costly to serve, tend to buy more and be more likely to promote the organization in their social network and thus bring in new customers through word of mouth (De Caigny et al. 2018).
Social networks in online and mobile games are of great importance for the players and their experience (Wallner et al. 2019; Loria et al. 2021). They appear for example as communities and clans of players in multiplayer online and battle games and in social network games that integrate the social features of the digital social network on which they are built (Klauser et al. 2013). They have been shown to affect in-game performance, frequency and length of play and engagement, to name a few (Rattinger et al. 2016; Pirker et al. 2018).
Based on our previous research, information from social networks provides alternative and significant information when predicting churn in the telecommunication industry (skarsdttir et al. 2016, 2017). Our research has established the importance of considering networks and the rich information contained in the relationships between entities, since they are often subject to influence from their neighbours and connected entities show similar behavior, which can be explained by homophily and assortativity skarsdttir et al. (2017, 2016, 2021, 2019, 2018). Indeed, features extracted from the social network of customers representing the churn status of their contacts are highly predictive of churn. However, the importance of networks has not been fully studied in the mobile gaming industry, and in particular not in relation to player churn.
The rest of this paper is organized as follows. In the next section, we discuss the literature related to social networks in gaming, on the one hand, and, churn prediction in online and mobile gaming, on the other hand. Then we present our methodology followed by the results of our experiments. The paper concludes with a discussion and steps for future work.
Massive multiplayer online games have also been frequently studied, including an analysis of social groups in such a game (Schiller et al. 2018), and using a modelling approach through a graph model that captures social connections and can be used for matchmaking in the game (Jia et al. 2015). A summary of the state-of-the-art in network analysis of massive multiplayer online games pointed out that some of the biggest challenges in analysis of such networks are the network dynamics, massive datasets and real life connections, which are vital for game development (Schlauch and Zweig 2015).
From this overview of the literature, we see that networks of players in online and mobile games in relation to churn are understudied, although it is known that social influence is an important factor. This is especially the case for free-to-play games.
This was clear in our data. Many players only played the game for a few days and some even played the game for a single day only. This made it apparent that the first week or two are the most crucial time when trying to predict and even prevent churn. Understanding the difference between the behavior of players labelled as churners and non-churners during those first days was therefore crucial for our purposes.
We adopted the following method to define churn. We considered the first 14 active days of a player as their observation period. We considered the subsequent 14 days as the prediction period. Furthermore, because of the bursty behavior, and to ensure we had enough activity data, we required the players to have played for at least a fixed number of days in their observation period, i.e. their first 14 days of game play, to be included in our analyses. We considered two cases for the required activity in the observation period, namely at least 3 and at least 5 days. If the player played no games in the prediction period, they were labelled as churners and otherwise, they were labelled as non-churners. This is shown for a few examples in Fig. 1, based on the 3 day activity requirement.
Network features in online and mobile games can be extremely important for competitive games, as detailed above. Having friends playing the same game can make it more enjoyable but also challenges the players.
In the mobile game we study here, the players have a choice to play with a similar player or a person from their list of friends. We considered two types of matches: friend and similarity, that result in two types of networks: the friend network and the similarity network. The friend network is an explicit social network, whereas the similarity network is an implicit social network, where nodes are connected based on similar properties, that is, skill level and geographic location. They are constructed in the same way: When two players play a match, an edge is created between them. The nodes are the two players.
We used the 10 cohorts of players to create the two networks. As seen in Fig. 2, these networks include, in addition to the players we are studying, other players that did not install the game during the 10 days mentioned. In the figure, the players under consideration are shown in dark gray. Their neighbors, i.e. the players they were matched with, are shown in light gray and white. The players that fulfill our definition of being churners are gray with a red circle, those that fulfill the definition of being non-churners are gray and those which we do not know the status of are shown in white. Their label is unknown. Figure 3 shows the two networks represented as a two layer network, with dashed lines connecting the same player the two layers. The top layer shows the friend network and the bottom layers shows the similarity network.
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