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Taherdoost, H. Enhancing Social Media Platforms with Machine Learning. Encyclopedia. Available online: https://encyclopedia.pub/entry/45753 (accessed on 17 November 2024).
Taherdoost H. Enhancing Social Media Platforms with Machine Learning. Encyclopedia. Available at: https://encyclopedia.pub/entry/45753. Accessed November 17, 2024.
Taherdoost, Hamed. "Enhancing Social Media Platforms with Machine Learning" Encyclopedia, https://encyclopedia.pub/entry/45753 (accessed November 17, 2024).
Taherdoost, H. (2023, June 17). Enhancing Social Media Platforms with Machine Learning. In Encyclopedia. https://encyclopedia.pub/entry/45753
Taherdoost, Hamed. "Enhancing Social Media Platforms with Machine Learning." Encyclopedia. Web. 17 June, 2023.
Enhancing Social Media Platforms with Machine Learning
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This entry is adapted from the peer-reviewed paper 10.3390/a16060271

Network analysis aids management in reducing overall expenditures and maintenance workload. Social media platforms frequently use neural networks to suggest material that corresponds with user preferences. Machine learning is one of many methods for social network analysis. Machine learning algorithms operate on a collection of observable features that are taken from user data. Machine learning and neural network-based systems represent a topic of study that spans several fields. Computers can now recognize the emotions behind particular content uploaded by users to social media networks thanks to machine learning.

social media artificial neural networks machine learning social networks

1. Introduction

Machine learning is a process of autonomous learning that occurs through the processing of typically very large data sets according to a statement by L’heureux et al. [1]. The techniques of the past, referred to as “symbolic artificial intelligence (AI),” were based on algorithms consisting of logical sets of instructions for encoding a given output (typically referred to as the target) for all potential inputs. In contrast, the new machine learning algorithms “learn” directly from data and estimate mathematical functions that discover representations of an input or learn to link one or more inputs to one or more outputs to make predictions using new data [2].
In recent years, the application of machine learning has gained traction across various disciplines in the social sciences. For instance, in the field of economics, researchers such as Varian [3], Blumenstock et al. [4], Athey and Imbens [5], and Mullainathan and Spiess [6] have incorporated machine learning methods into their studies. Similarly, in political science, Bonikowski and DiMaggio [7] have explored the use of machine learning techniques. In sociology, scholars such as Baldassarri and Abascal [8] and Evans and Aceves [9] have applied machine learning in their research. Communication science has also embraced machine learning with studies conducted by Bail [10]. Furthermore, machine learning has found practical applications in the public administration sector (Athey [5] and Berk et al. [11]), as well as in the operations of private companies.
Kleinberg et al. [12] state that machine learning encompasses a wide variety of approaches and instruments. The function of user-generated content, which is also subject to feedback from other users [13][14], is expanding as a result of the proliferation of social media. Given that social networking sites (SNSs) offer abundant opportunities for social comparison [15], researchers have begun to investigate their implications for psychological health [16]. By spending a great deal of time observing the posts of others, users are inevitably drawn into the process of social comparison, particularly when using SNSs devoted to visual content, such as Instagram [17]. Social comparison research investigates how people respond when comparing themselves to others. It distinguishes between upward and descending comparisons. When comparing themselves to others negatively, people make an upward comparison. By comparing themselves to those they perceive to be superior, individuals may experience unpleasant and agonizing emotions, or they may be motivated to better themselves. A downward comparison occurs when individuals compare themselves favorably to those they perceive to be less fortunate. It can aid in restoring not only damaged self-esteem, but also joy and pride [18]. Thus, neither comparison is always uncomplicated, and they can have both positive and negative effects on the formation of self-evaluation and identity [18][19].
It is an undeniable fact that digitalization is altering the conventional procedures and balances of the current social and economic organizational model [20]. Approximately 15 percent of the city’s annual budget is allocated to the implementation of these measures. Therefore, it is necessary to conduct an exhaustive analysis of the information on the crimes that have occurred so that the lines of preventative action can be focused on the most affected areas [21].
The advent of social networks has greatly enhanced global communication among Internet users. The analysis of social networks plays a vital role in summarizing the interests and opinions of users (referred to as nodes), uncovering interaction patterns (referred to as links) between users, and extracting valuable insights from the events occurring on online platforms. The information gleaned from social network analysis holds immense potential for various applications. Some notable examples include targeted online advertising [22], personalized recommendations [23], viral marketing [24], social healthcare [25], analysis of social influence [26], and studying academic networks [27].
Machine learning and neural networks have exhibited remarkable capabilities in processing vast amounts of data, identifying patterns, and making predictions with remarkable accuracy [28]. However, as their influence permeates society, it becomes crucial to examine the social considerations associated with their deployment, particularly in the context of media and networks.

2. Applications of Machine Learning and Neural Networks in Social Fields

Several disciplines, including social domains, have been revolutionized by machine learning and neural networks. In the field of social media analysis, machine learning techniques facilitate the extraction of valuable insights from vast amounts of data, such as sentiment analysis [29], trend identification, and the detection of fake news. In addition, neural network-powered recommendation systems provide personalized content suggestions based on user behavior, thereby enhancing the user experience.
Understanding and analyzing social media posts, remarks, and reviews is impossible without NLP. Using neural networks such as recurrent neural networks or transformers, machine learning algorithms enable tasks such as sentiment analysis, topic modeling, and text classification. By deciphering the meaning of textual data, NLP enables a more in-depth comprehension of user sentiments and preferences.
Similarly, machine learning and neural networks are indispensable for social network analysis. These techniques can analyze the intricate structures of social networks, identify influential users, detect communities, and predict individual relationships. Graph neural networks (GNNs) are especially efficient at modeling and comprehending social network data, thereby revealing valuable insights and connections.
The applications of machine learning extend beyond social media to social welfare and humanitarian efforts. Using predictive models and neural networks, machine learning supports disaster responses, public health initiatives, and humanitarian aid resource allocation. These technologies can predict disease outbreaks, identify vulnerable areas during natural disasters, and optimize relief efforts, ultimately sparing lives and reducing suffering. The applications of machine learning and neural networks in various social disciplines are summarized in Table 1.
Table 1. Applications of machine learning and neural networks in social fields.
Field Applications
Social Media Analysis Sentiment analysis
Trend identification
Fake news detection
Recommendation Systems Personalized content suggestions based on user behavior
NLP Sentiment analysis
Topic modeling
Text classification
Social Network Analysis Influential user identification
Community detection
Relationship prediction
Social Good and Humanitarianism Disaster response
Public health initiatives
Resource allocation for humanitarian aid
Online Advertising Targeted advertising based on user preferences and behavior
Personalized Education Adaptive educational content based on individual learning styles
Fraud Detection Detection of fraudulent activities such as credit card fraud and online scams
Cybersecurity Network traffic analysis for detecting anomalies and identifying cyber threats
Mental Health Analysis Identification of individuals at risk of mental health issues through social media analysis
Various machine learning algorithms and neural network architectures have been utilized for social network analysis and content recommendation systems, each with its own strengths and limitations. Traditional algorithms, such as logistic regression and decision trees, offer interpretability and simplicity, making them suitable for comprehending relationships and making explicit feature-based predictions. However, their efficacy may be limited when presented with complex data or feature spaces with high dimensions. Alternatively, deep learning architectures such as convolutional neural networks and recurrent neural networks excel at capturing intricate patterns and temporal dependencies, making them useful for tasks such as sentiment analysis and sequence modeling. To achieve optimal performance, however, these architectures frequently require substantial computational resources and vast quantities of labeled data. In addition, their black-box nature hinders interpretability, which is vital for certain applications such as elucidating recommendations or identifying biased patterns. Hybrid approaches that combine the strengths of various algorithms and architectures, such as ensemble methods and hybrid deep learning models, have emerged as promising solutions, enabling improved accuracy, interpretability, and scalability in social network analysis and content recommendation systems. The optimal strategy is determined by the specific mission, available data, interpretability needs, and computational resources.

References

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  3. Varian, H.R. Big data: New tricks for econometrics. J. Econ. Perspect. 2014, 28, 3–28.
  4. Blumenstock, J.; Cadamuro, G.; On, R. Predicting poverty and wealth from mobile phone metadata. Science 2015, 350, 1073–1076.
  5. Athey, S.; Imbens, G.W. The state of applied econometrics: Causality and policy evaluation. J. Econ. Perspect. 2017, 31, 3–32.
  6. Mullainathan, S.; Spiess, J. Machine learning: An applied econometric approach. J. Econ. Perspect. 2017, 31, 87–106.
  7. Bonikowski, B.; DiMaggio, P. Varieties of American popular nationalism. Am. Sociol. Rev. 2016, 81, 949–980.
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  9. Evans, J.A.; Aceves, P. Machine translation: Mining text for social theory. Annu. Rev. Sociol. 2016, 42, 21–50.
  10. Bail, C.A. The cultural environment: Measuring culture with big data. Theory Soc. 2014, 43, 465–482.
  11. Berk, R.; Heidari, H.; Jabbari, S.; Kearns, M.; Roth, A. Fairness in criminal justice risk assessments: The state of the art. Sociol. Methods Res. 2021, 50, 3–44.
  12. Kleinberg, J.; Ludwig, J.; Mullainathan, S.; Obermeyer, Z. Prediction policy problems. Am. Econ. Rev. 2015, 105, 491–495.
  13. Mingoia, J.; Hutchinson, A.D.; Wilson, C.; Gleaves, D.H. The relationship between social networking site use and the internalization of a thin ideal in females: A meta-analytic review. Front. Psychol. 2017, 8, 1351.
  14. Verduyn, P.; Ybarra, O.; Résibois, M.; Jonides, J.; Kross, E. Do social network sites enhance or undermine subjective well-being? A critical review. Soc. Issues Policy Rev. 2017, 11, 274–302.
  15. Yang, C.-C. Instagram use, loneliness, and social comparison orientation: Interact and browse on social media, but don’t compare. Cyberpsychology Behav. Soc. Netw. 2016, 19, 703–708.
  16. Lup, K.; Trub, L.; Rosenthal, L. Instagram# instasad?: Exploring associations among instagram use, depressive symptoms, negative social comparison, and strangers followed. Cyberpsychology Behav. Soc. Netw. 2015, 18, 247–252.
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  21. Alegre, R. Policías Comunales y Policías Locales: Un Estudio de la Seguridad en Una Ciudad del Interior de la Provincia de Buenos Aires. 2019.
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Hamed Taherdoost
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