Meta

Once a dominant force in the social media landscape, Meta Platforms, Inc. has in recent years shifted its focus to a futuristic vision of the metaverse. Yet, a closer look at the company reveals a host of deep-seated issues—from a copycat product strategy and declining user interest to a concerning corporate culture marked by systemic bias, questionable hiring practices, and ethically dubious international operations. These problems suggest a company struggling to reconcile its grand ambitions with the fundamental failings of its core business.

A defining characteristic of Meta’s product development has been its tendency to imitate successful rivals rather than innovate. The creation of Instagram Stories, a direct response to Snapchat, and the launch of Reels to compete with TikTok are prime examples of this clone and conquer approach. While these efforts have achieved commercial success, they have also contributed to a perception of creative stagnation. This lack of originality has coincided with a notable decline in interest in the main Facebook platform, particularly among younger demographics who now view it as a space for older generations filled with irrelevant, low-quality content and a constant stream of advertising.

The company's massive investment in artificial intelligence has yet to yield a breakout product commensurate with the hype and cost. While Meta has poured billions into AI research, its public-facing AI applications often fall short of expectations, especially when compared to its competitors. This disconnect is further highlighted by controversial corporate practices, including a hiring philosophy that offers exorbitant salaries to top talent while simultaneously engaging in sweeping, performance-based layoffs. Many former employees have publicly challenged this narrative, suggesting the cuts were more about cost-efficiency than a genuine reflection of employee contributions.

Perhaps most troubling are the well-documented ethical and cultural shortcomings within the company. Meta has faced multiple lawsuits and regulatory actions for its racist and discriminatory practices, particularly concerning its advertising algorithms. The company has been accused of digital redlining for steering ads for for-profit colleges disproportionately to Black users and was forced to settle a Department of Justice lawsuit over its discriminatory housing ad delivery system. Internationally, Meta's pursuit of growth has had tragic consequences. In multiple countries, including Myanmar and Ethiopia, the company's algorithms and lack of local-language content moderators have been linked to the amplification of hate speech and incitement of mass violence. This shows a stark disregard for human rights and safety in its pursuit of profit and market expansion.

The promises of a new, innovative era under the Meta banner appear to be overshadowed by a trail of systemic failures. The company's reliance on copying competitors, its declining user base, the questionable ROI of its AI initiatives, and a corporate culture marred by discrimination and unethical practices have created a significant crisis of confidence. To secure a future beyond its current reputation, Meta must first confront the deep-seated issues in its present.

Future of Social Media

Social media has long transcended its origins as a tool for connecting with friends, becoming a fundamental pillar of public discourse, commerce, and identity. However, as platforms like Facebook, Instagram, and X have matured, they face an increasingly complex future defined by external pressures from legal frameworks and internal challenges from their own algorithms. The next phase of social media’s evolution will be a tightrope walk between fostering engagement and upholding user rights in a world of ever-changing free speech laws and inherent algorithmic biases.

Globally, the legal landscape is forcing a re-evaluation of social media's role. In the past, platforms largely operated as private entities with broad immunity from liability for user-generated content under laws like Section 230 in the United States. Today, new regulations such as the European Union’s Digital Services Act (DSA) and the UK’s Online Safety Bill are introducing strict obligations for platforms to moderate illegal content, combat misinformation, and increase transparency. These laws, while intended to protect users, create a tension with the principle of free speech. They push platforms to either adopt more aggressive moderation policies to avoid hefty fines or face legal challenges that could fundamentally alter their business models. The result is a fragmented and often inconsistent approach to content moderation, where what is acceptable in one country may be removed in another, leaving platforms in a difficult position and users confused about their rights.

Simultaneously, the very engines that drive user engagement—the recommendation algorithms—are under intense scrutiny. These systems are designed to maximize time on-platform by feeding users content that is likely to hold their attention, which often leads to the amplification of polarizing, emotionally charged, or controversial material. This algorithmic bias, which can be rooted in historical data or the system’s design, can create filter bubbles and echo chambers that limit users' exposure to diverse perspectives and reinforce existing beliefs. Critics argue that this not only degrades the quality of public discourse but also indirectly threatens users' fundamental rights to access information and participate in an open marketplace of ideas. The constant pressure to engage also risks users' mental well-being, as platforms are optimized to leverage cognitive biases rather than promote healthy interaction.

The future of social media will therefore be shaped by a fundamental power struggle: the need for platforms to remain profitable by maximizing engagement, the imperative to comply with new regulations, and the growing demand from users and governments for ethical and transparent systems. The next era of social networks will likely see a move towards decentralized platforms, a greater emphasis on user-configurable feeds, and more transparent algorithmic practices. The challenge lies in building a new model that can balance the commercial incentives of platforms with the public interest of a free, informed, and inclusive digital society.

Microsoft Teams

Microsoft Teams, once positioned as the ultimate all-in-one collaboration solution, has, for many users and organizations, devolved into a source of considerable frustration. While its comprehensive feature set promises streamlined communication and productivity, the reality often presents a clunky, resource-intensive, and sometimes counterintuitive experience that hampers rather than helps daily workflows. The promise of seamless integration frequently gives way to a labyrinth of notifications, performance issues, and an often-confusing user interface.

One of the most common complaints leveled against Microsoft Teams is its notorious resource consumption and performance issues. Users frequently report that Teams is a significant drain on system memory and CPU, leading to sluggish performance, especially on older or less powerful machines. This can manifest as slow loading times, delayed messages, and a general unresponsiveness that disrupts the flow of work. For applications designed to enhance real-time communication, such performance lags are critical flaws, undermining the very efficiency they aim to deliver. The constant need for updates, which often require restarting the application or even the entire system, further adds to the frustration and perceived unreliability.

Another significant drawback lies in its often cluttered and overwhelming user interface (UI). While attempting to be a single hub for chat, meetings, file sharing, and app integration, Teams can feel like a chaotic amalgamation of features. The sheer volume of channels, notifications, and integration points can quickly lead to information overload. Important messages can get lost in the noise, and navigating between different functions can be cumbersome. This lack of clear hierarchy and intuitive design means users often spend valuable time searching for specific information or features, rather than focusing on their actual tasks. The notification system, while configurable, often errs on the side of excessive alerts, contributing to digital fatigue.

Furthermore, despite being a Microsoft product, integration with other Microsoft applications isn't always as seamless or intuitive as one might expect. While Word, Excel, and PowerPoint files can be shared and co-edited within Teams, the experience can still be less fluid than working directly within the native applications. The file management system, though ostensibly SharePoint-backed, can feel disjointed and less user-friendly than a dedicated cloud storage solution. This perceived friction, rather than creating a harmonious ecosystem, can lead to users reverting to older, more familiar methods of working outside of Teams.

Finally, the learning curve and administrative overhead can be surprisingly steep, particularly for organizations new to the platform or those without dedicated IT support. Setting up channels, managing permissions, integrating external apps, and customizing workflows often require a deeper understanding than casual users possess. This administrative burden detracts from core business activities and can lead to underutilized features or misconfigurations that further exacerbate user frustration.

In essence, while Microsoft Teams aims to be a comprehensive digital workspace, its execution often falls short of expectations. Its performance issues, cluttered interface, imperfect integrations, and administrative complexities can transform it from a promised productivity boon into a daily source of technological friction, leaving many users questioning its true value in the modern collaborative landscape.

The Hypocrisies of Elon Musk

Elon Musk, a figure synonymous with audacious innovation and futuristic visions, has cultivated an image as a maverick disruptor, challenging the status quo and striving for a better future for humanity. Yet, this carefully crafted persona often clashes with his actions and pronouncements, leading many to label him a hypocrite. The chasm between his lofty ideals and practical behaviors raises questions about the true motivations behind his ventures and the consistency of his moral compass.

One of the most glaring perceived hypocrisies lies in his championing of environmental sustainability while simultaneously engaging in activities that contradict this stance. Musk's Tesla has revolutionized electric vehicles, and his efforts with SolarCity aimed to accelerate renewable energy adoption. However, his significant involvement in SpaceX, a company that launches rockets relying on fossil fuels, presents a stark contrast. While space exploration has its own merits, the environmental impact of rocket launches, particularly in their current form, is considerable and seems to stand in tension with a purely green agenda. Furthermore, his previous flirtations with and recent acquisition of a social media platform, which became embroiled in content moderation controversies, often saw him prioritize "free speech absolutism" over addressing harmful narratives, an approach that can inadvertently undermine the societal stability arguably necessary for long-term environmental progress.

Another area of apparent contradiction emerges in his advocacy for human advancement and the betterment of society, often through technological leaps. Musk speaks passionately about colonizing Mars to ensure humanity's survival and developing Neuralink to augment human capabilities. However, critics point to the working conditions within some of his factories, which have faced accusations of demanding environments, high injury rates, and anti-union sentiments. If the goal is to elevate humanity, some argue, it should begin with ensuring the well-being and equitable treatment of the very individuals contributing to these grand visions. The pursuit of futuristic technologies at the expense of current labor welfare creates a disconnect between rhetoric and reality.

His position on government subsidies and free market principles also presents a perceived inconsistency. While he often extols the virtues of competition and individual enterprise, his companies, particularly Tesla and SolarCity in their early stages, have significantly benefited from substantial government incentives, tax breaks, and regulatory credits. While these subsidies were designed to jumpstart nascent industries, critics highlight that Musk, a vocal proponent of minimal government intervention, has nonetheless leveraged considerable public funds to build his empires. This reliance on state support, followed by a critique of government bureaucracy, strikes many as disingenuous.

Finally, Musk's often-chaotic and uninhibited use of social media, particularly his former platform X (formerly Twitter), frequently undermines his image as a serious visionary. His engagement in online feuds, dissemination of controversial opinions, and sometimes impulsive statements, often appear to prioritize spectacle over thoughtful discourse. For someone who leads companies at the forefront of global innovation, his online behavior can seem frivolous, even detrimental, to the very societal progress he claims to champion.

Elon Musk's public narrative is one of a tireless innovator pushing humanity forward. Yet, a closer examination reveals a pattern of perceived contradictions: environmental advocacy alongside polluting ventures, calls for human betterment while facing criticism over labor practices, and appeals for free markets while benefiting from government aid. These perceived hypocrisies do not necessarily invalidate his technological achievements, but they certainly complicate his standing as an unambiguous force for good, inviting skepticism about the alignment of his words with his deeds.

Immersive AI

The evolution of artificial intelligence has consistently pushed the boundaries of what machines can do, from complex data analysis to natural language processing. However, the true revolution lies not just in AI's capabilities, but in how we interact with it. This brings us to the field of immersive AI – a powerful convergence of artificial intelligence with virtual reality (VR), augmented reality (AR), and mixed reality (MR) technologies. This synergy promises to transcend traditional screen-based interactions, ushering in an era where digital experiences are not merely viewed, but truly inhabited, making immersive AI the undeniable next frontier of the future.

At its core, immersive AI leverages AI's intelligence to create dynamic, responsive, and believable virtual or augmented environments. Unlike static digital worlds, an immersive AI-driven experience adapts to the user in real-time. Imagine stepping into a virtual training simulation where AI-powered characters respond realistically to your actions, or an AR application that intelligently overlays information onto your physical surroundings, anticipating your needs. Here, AI isn't just a backend algorithm; it's an active participant, generating realistic physics, intelligent non-player characters (NPCs) with believable personalities, adaptive narratives, and personalized content that evolves with each user's engagement. It transforms passive observation into active, intuitive, and deeply engaging participation.

The "why" behind immersive AI being the next frontier stems from its profound potential to redefine human-computer interaction and unlock unprecedented applications across various sectors. Firstly, it offers an unparalleled level of presence and immersion. By engaging multiple senses and allowing for natural spatial interaction, immersive AI reduces the cognitive load typically associated with digital interfaces. Users no longer need to translate their intentions through keyboards or touchscreens; they can simply act within the digital space, much as they would in the real world. This naturalness makes technology more accessible and intuitive.

Secondly, the personalization and adaptability afforded by AI within these environments are transformative. AI can learn from user behavior, preferences, and even emotional states, tailoring experiences dynamically. In education, this could mean personalized virtual tutors that adapt teaching methods to individual learning styles. In healthcare, it could involve therapeutic environments that respond to a patient's progress during rehabilitation, or highly realistic surgical simulations that offer adaptive challenges.

Beyond these, the applications are vast and revolutionary. In entertainment, immersive AI will lead to games with truly intelligent characters and endlessly replayable, branching storylines, or virtual concerts where the audience's energy influences the performance. For design and engineering, collaborative virtual workspaces powered by AI could enable global teams to interact with 3D models as if they were physically present, accelerating prototyping and innovation. Even in social interaction, immersive AI promises more natural and meaningful virtual communication, bridging geographical distances with a sense of shared presence.

While challenges such as data privacy, ethical AI development, and the computational demands of such systems remain, the trajectory is clear. Immersive AI represents a fundamental shift from merely consuming digital content to actively living within intelligent digital realms. It promises a future where technology is not just a tool, but an extension of our reality, offering richer experiences, more efficient learning, and entirely new forms of human connection. This profound re-imagining of interaction makes immersive AI not just a technological advancement, but the very essence of the next digital frontier.

What is Engagement Farming

In the ever-evolving landscape of social media, where visibility is currency, a darker side of digital marketing has emerged: engagement farming. This refers to the practice of using manipulative and often unethical tactics to artificially inflate engagement metrics on social media platforms. These metrics, including likes, comments, shares, and followers, are crucial for perceived popularity and influence. While genuine engagement fosters community and connection, engagement farming prioritizes quantity over quality, often with detrimental consequences.

At its core, engagement farming exploits the algorithms that govern social media feeds. These algorithms prioritize content with high engagement, assuming that popular content is inherently valuable. By artificially boosting these metrics, engagement farmers can increase their content's visibility, reaching a wider audience than they organically would. This can be used to promote products, services, or ideas, often with misleading or deceptive tactics.

Several techniques fall under the umbrella of engagement farming. One common method is "clickbait," which involves using sensationalized or misleading headlines and thumbnails to lure users into clicking on content. This content often fails to deliver on the promises made in the headline, leaving viewers feeling deceived. Another tactic is "engagement baiting," where creators explicitly ask for likes, comments, or shares, often using emotional manipulation or contests. For instance, a post might say, "Like this if you love your mom,"guilting users into interacting.

"Follow/unfollow" is another prevalent technique, where users rapidly follow and unfollow numerous accounts to gain followers. The hope is that a portion of those followed will reciprocate, inflating the follower count. "Comment pods" or "engagement groups" involve groups of users who agree to like and comment on each other's posts, creating an artificial sense of popularity. Some even resort to purchasing fake engagement from "click farms," where low-paid workers or bots create fake likes, comments, and followers.

The consequences of engagement farming are far-reaching. Firstly, it distorts the authenticity of online interactions. Genuine engagement reflects genuine interest, while farmed engagement creates a false impression of popularity. This can mislead users into trusting or supporting content that is not truly valuable or credible. Secondly, it can lead to the spread of misinformation. Engagement farmers may use sensationalist or misleading content to drive interaction, regardless of its accuracy. This can have serious consequences, particularly in areas like news and public health.

Moreover, engagement farming can harm the reputation of individuals and brands. When audiences discover that an account's engagement is artificially inflated, they may lose trust and credibility. Social media platforms are also cracking down on these practices, with algorithms designed to detect and penalize fake engagement. Accounts caught engaging in such tactics may face reduced visibility or even suspension.

Engagement farming is a deceptive practice that undermines the integrity of social media. While the allure of quick growth and increased visibility may be tempting, the long-term consequences can be damaging. As users become more discerning and platforms refine their algorithms, the effectiveness of engagement farming is likely to diminish. The focus should instead be on building genuine connections and creating valuable content that fosters organic engagement.

Meta: Copycat and Idea Snatcher

Meta, the tech giant behind Facebook, Instagram, and WhatsApp, has undeniably shaped the landscape of social media. However, its approach to innovation has often been called into question, with critics arguing that the company heavily relies on replicating successful ideas from competitors rather than fostering genuine creativity.

One of the primary reasons for this perception is Meta's tendency to incorporate features popularized by rival platforms. A notable example is the introduction of "Stories" on Facebook and Instagram, a feature initially popularized by Snapchat. Similarly, Instagram's "Reels" bears a striking resemblance to TikTok's short-form video format. These instances suggest a pattern of adopting proven concepts rather than developing entirely novel ones.

This strategy can be attributed to several factors. Firstly, in the fast-paced world of social media, companies are under immense pressure to maintain user engagement and stay ahead of trends. Copying successful features can be a quick and effective way to achieve this, ensuring that users don't migrate to competing platforms. Secondly, Meta's vast resources and market dominance may incentivize this approach. With the ability to quickly scale and integrate new features across its platforms, the company may prioritize rapid implementation over the risks and uncertainties associated with original innovation.

However, this approach is not without its drawbacks. While it may lead to short-term gains in user engagement, it can stifle genuine innovation and lead to a homogenization of the social media landscape. When companies prioritize replication over originality, it can discourage the development of truly unique and groundbreaking features. This can limit user choice and ultimately hinder the evolution of online communication and interaction.

Moreover, Meta's practice of adopting competitor ideas raises ethical questions about intellectual property and fair competition. While adapting existing concepts is a common practice in many industries, the line between adaptation and outright copying can be blurry. Critics argue that Meta's approach sometimes crosses this line, potentially harming smaller, more innovative companies that lack the resources to compete.

While Meta's strategy of incorporating successful features from competitors has contributed to its continued dominance in the social media industry, it has also fueled concerns about a lack of original innovation. The company's actions raise important questions about the balance between competition and creativity, and the long-term implications for the future of social media.

Chinese Social Media

Forget Facebook and Twitter; the digital landscape in China operates on its own vibrant ecosystem, a fascinating blend of familiar concepts with uniquely Chinese characteristics. Navigating this "Dragon's Digital Playground" reveals a collection of social networking apps and sites that are not just alternatives to their Western counterparts, but often more integrated, feature-rich, and deeply embedded in daily life. 

At the apex of this digital realm sits WeChat (Weixin), the undisputed king. More than just a messaging app, WeChat is a Swiss Army knife of social interaction. Imagine WhatsApp, Facebook, PayPal, and a mini-app store all rolled into one. Its ubiquity makes it indispensable for communication, commerce, and practically every facet of modern Chinese life. 

Then there's Sina Weibo While it shares the microblogging format, Weibo is a more open platform, a public square where trending topics explode, celebrity gossip spreads like wildfire, and brands engage with massive audiences. Its multimedia capabilities and emphasis on news and public discourse differentiate it from its Western cousin, making it a crucial platform for real-time information and viral content. 

For the visually inclined, Xiaohongshu (Little Red Book) offers a captivating blend of Instagram and Pinterest, with a strong emphasis on lifestyle and e-commerce. This platform thrives on user-generated content, particularly product reviews, fashion tips, and travel recommendations. Its highly engaged community, predominantly young women, turns to Xiaohongshu for inspiration and authentic opinions, making it a powerful platform for brands targeting this demographic. 

The short-video craze has found its champion in Douyin, the domestic version of TikTok. While sharing the addictive format of short, looping videos, Douyin boasts a massive and highly active user base within China. Its sophisticated algorithm keeps users hooked on a personalized feed of entertainment, trends, and even live-streaming e-commerce, making it a dominant force in capturing attention and driving consumer behavior. 

Beyond these giants, a diverse range of platforms cater to niche interests. Bilibili has cultivated a thriving community around anime, comics, and games (ACG), offering a unique space for younger generations to connect over shared passions. Zhihu, akin to Quora, serves as a knowledge-sharing platform where users ask and answer questions on a vast array of topics, fostering intellectual discussions and expert insights. Even older platforms like Tencent QQ still hold sway, particularly among younger demographics and in smaller cities, offering instant messaging and social networking features. 

Comparing these platforms reveals a fascinating landscape shaped by China's unique internet regulations and cultural preferences. Unlike the West, where a few dominant players often span various functionalities, Chinese social media tends towards specialization and deep integration within specific ecosystems. The emphasis on mobile-first design from the outset has also led to highly intuitive and feature-rich apps that often surpass their Western counterparts in terms of integrated services. 

Navigating the Dragon's Digital Playground requires understanding these nuances. Each platform offers unique opportunities for individuals and businesses to connect, share, and engage. From the all-encompassing power of WeChat to the trend-setting influence of Xiaohongshu and the viral reach of Douyin, the Chinese social media scene is a dynamic and captivating world, constantly evolving and offering a glimpse into the digital habits of a massive and engaged online population.

Future of Social Media Networks

The future of social media networks is poised for a significant evolution, driven by technological advancements and shifting user expectations. Expect a more personalized and immersive experience, where AI-powered algorithms curate content based on individual preferences and real-time interactions.

Augmented and virtual reality will blur the lines between the digital and physical worlds, enabling users to interact in shared virtual spaces, attend immersive events, and experience content in entirely new ways. Social interactions will become more fluid, with seamless transitions between virtual and physical environments. 

Decentralization will gain traction, offering users greater control over their data and content. Blockchain-based platforms will prioritize privacy and transparency, empowering individuals to monetize their creations and participate in governance.

Furthermore, expect a shift towards niche communities and interest-based platforms. General-purpose social networks will give way to specialized spaces catering to specific hobbies, professions, and passions. This fragmentation will foster deeper connections and more meaningful interactions. 

AI-driven moderation will become increasingly sophisticated, combating misinformation and harmful content more effectively. However, ethical considerations surrounding AI bias and censorship will remain paramount.

Ultimately, the future of social media will be characterized by greater personalization, immersive experiences, and user empowerment. The platforms that thrive will be those that prioritize privacy, foster meaningful connections, and adapt to the evolving needs of their users.

Cheating Culture in Asian Societies

• How reputation concerns and confucian values influence cheating behavior
• Impressions on sexual unfaithfulness and their accuracy show a degree of universality
• Factors Contributing to Infidelity in Marriage Within Asian Countries A Systematic Review of Literature
• Why 84 Percent of Women in Japan Think Cheating is Healthy?
• What is Cheating Culture in Japan Really Like?
• Adultery and Infidelity in China
• Half of married Korean men have cheated: study
• Our Cheating Hearts
• Korean Political Scandal

Dumb Things People Say to Each Other

• Walking up to someone sleeping and asking them, "are you asleep?"
• Calling someone and saying, "you're not asleep", like would they really be picking up the phone if they were?
• Asking someone in the morning, "are you awake?"
• Giving some wholesome advice like, "Why don't you just get a better job?"
• Asking someone, "how are you", especially when they are sick
• Asking someone, "are you ok", when they obviously don't look ok
• Telling someone who has lost everything, "it'll be ok."
• "I know exactly how you feel", when they have never experienced it before
• Telling someone, "you'll find someone better", after a breakup
• Someone getting told, "you look tired"
• Using phrases like, "you should just..."
• "I am here for you if you need anything", coming from someone who is never there for you
• "I'm not racist, but...", then followed by a racist statement
• When someone says, "you should just...relax"
• "It's all in your head", because nothing is ever in your head
• "I'm just being honest", like they are never honest
• "I'm not good with names", used especially when someone can't be bothered with your name
• "I'm sure you'll get over it"
• "I'm not a fan of..."
• "I'm just being realistic", does that mean they are usually unrealistic
• "You should just try to be happy"
• "I told you so"
• "Just be yourself", when are you ever not yourself?
• "I'm not sexist, but...", then followed by a sexist statement
• "Trust me...", like that is all the more reason not to trust you, pretty much the only time someone says that is when they doing something dubious, and why should you trust someone you don't even know just because they expect you to blindly trust them

Meta Shuts Down AI Character Accounts

Meta Shuts Down AI Character Accounts

OnlyFans

What types of people are on OnlyFans?

Consumers:

• Individuals that are looking for sexually explicit content who may also want exclusive access.
• People that also have interests in other converging and diverse areas.
• It is often stated that the platform caters to incels which may indirectly promote violence and misogyny.

Content Creators:

• Generally, described variously as sex workers that produce sexually explicit content for consumers.
• These people can vary across their interest groups from fitness trainers, artists, musicians, social media influencers to models.
• Although, it is primarily known for sexual content, there may be other diverse content on the platform for a range of users.

Social Media Culture

Social media networks are widespread on the internet. However, this experience is good for some people but very bad for others. The bad experiences often lead to hightened states of depression. Social media invariably is all about popularity. But, this popularity is also overshadowed by not only influence but also the level of negative sentiments one can receive from people. In most cases, the person that subscribes or follows a person is totaly a stranger. This strangeness and unfamilarity of people leads to a very cold and defensive state of interaction especially among women. Removing someone from your follower/subscriber count may mean nothing for one person while could mean the world to another. In other cases, not getting reply from a person can be quite an issue for others. Other cases might involve blocking which the person might take quite personally. In general, people of celebrity status have had it quite easy as they already can gain plenty of followers from just influence or even hiring a separate marketing agency to manage their social accounts. However, other people likely would have to work towards it. People often may only reply to you based on your popularity as that would increase their follower count. Social media also seems to be a network of hierarchies. In many cases, it reflects the way people climb the social ladder, in associating with people who are more popular or influencial. When people get a very high follower or subscriber count they also have a tendency of becoming quite bigheaded and proud of their achievement. You never really know whether you are talking to a human or a bot on social media. And, whether someone's post is a scheduled post or something they directly replied to. In fairness, this could also be a reason why so many people are less empathetic. Social media as a result tends to be more about playing the game. It also seems to be a very cold place to hang out. Some people obviously don't care for popularity, influence, nor take the whole experience that seriously and this likely also negatively impacts their experience. Often the way the person looks also effects their popularity. Social media networks tend to be a breeding ground for shallow people, like an ego network. Recommendations are also geared towards popularity of content which further compounds the biases. No doubt social media can be addictive. But, it has also become a battle ground for people who want to display their frustrations, their hypocrisy, and be who they want to be outside of the confines of the real world. This often leads to some people being very unempathetic towards the people they interact and communicate, often with an unconscious bias. In many respects, social media reflects the real world, only worse. It also can be an opportunity to take a glimpse through the looking glass as to the reality of character and ideological mindset of people, especially as so many feel they can say whatever they like and treat people however they like without much regard for consequence. They are also great for mining data and analyzing human behavior. Take a step away from social media and see how the world suddenly feels simpler, more productive, likely less stressful, and frustrating.

Social Media Evolution

From Moments to Memories
From Images & Videos to Dynamic Stories
From Static Content to Reactions & Live In-Moment Feeds
From Messages to Communities
From Social Networks to Knowledge & Sentiment Search
...

Social Network Metrics

1. Engagement Metrics

• Amplification Metrics
• Applause Metrics
• Conversation Rate

2. Influence Metrics
3. Reach
4. Impression

• Total Audience
• Number of Unique Users
• Number of Active/Passive Users

Text-Driven Forecasting

Text-Driven Forecasting is about building systems that are able to predict on the future by analyzing collection of a body of natural language documents. Often they predict numeric quantities about a certain event based on various textual sources/feeds (e.g. news, twitter, facebook, polling data, opinion blogs, financial reports, amazon reviews, economics data, etc) as input and gather information gain from aspects of sentiment analysis and subjectivity. Machine Learning algorithms that can be applied to such a domain can range from regression, deep learning, decision trees, and others. 

Examples:
Predicting movie reviews using social media
Predicting opinion polls using social media
Predicting stock volatility using financial data
Predicting government elections and referendums
Predicting product sales using social media
Predicting property prices in the future
Predicting risk of a potential course of action or decision

smith whitepaper

Related Courses & Resources:
Priberam Labs
Social Media Analysis & Computational Social Science
Natural Language Processing & Social Interaction
Computational Social Science
Social & Information Network Analysis
Text as Data
NLP for Social Science
Computational Social Science
Computational Linguistics / Computational Social Science
Predicting Economic Indicators from Web Text Using Sentiment Composition
Making Predictions with Textual Contents

Complex Networks

The rising scale of data and the need for information gain has provided a greater need towards understanding patterns to form knowledgeable insights. In many cases, such patterns can be derived through machine learning and data mining. But, also through studying complex networks that form within contextual data. The below links provide useful sources of study in the science of complex networks.

The Structure and Dynamics of Networks

Networks, Crowds, and Markets

Networks: An Introduction

Understanding Social Networks

Six Degrees: The Science of Connected Age

Coursera: Networked Life

Coursera: Social Network Analysis

Network Science

Network Science Book

NetworkX

Complex networks have become a popular science of big data processing especially for the Web. NetworkX is one such library that provides a flexible option in the python environment to study such graphs in context. The library provides much scope towards large scale and real-world networks especially for social network analysis. The library can be amazingly useful for visualization alongside D3. It is also compatible with Sage and graphs can be integrated.

Quora

Quora is a site where questions and answers are collaboratively discussed and modified through a community of users. Essentially a social process. However, this social process is flawed and at times opinions are taken personally from users which means the site lacks integrity in respecting every ones' opinions and not of specific few. In that process, it discriminates on opinions for answers to questions. Which in a lot ways diminishings the whole collaborative and social aspect of the process. The model is flawed and the answers will often not be objective. It also means people will get into heated discussions when they take things too personally which can be derived as anti-social behavior by personal attacks on individuals. The moderation process is also very discriminating and perhaps can also be viewed as bigoted as their opinions on evaluation are also surmised in discriminating users. It seems in all intents and purposes if one cannot handle a variety of opinions then perhaps it is not the best place to be as one will often end up taking things too personally. There is a rather big social issue on the internet that arises from people taking the aspect of comments seriously losing the perspective and context of reality. It may even be taken as the fact that an opinion is every ones essential right whether it is agreeable or not. However, what turns into anti-social behavior is when personal attacks are taken on which is not very conducive to the openness of the social web on the basis of which the internet has been so successful. Perhaps, the view of social media and networks needs to be taken with a more open view by users when using such sites knowing that there will often be views that they may not agree with but having the sense of respect to understand that others have a right to hold their own opinions whether right or wrong. This is one failing factor of social web where people blur the lines between reality and internet and often end up taking things too personally. It is also perhaps why there needs to be a certain awareness of such issues as well as understanding that the global space like the internet necessarily will have variety of content not necessarily agreeable to all. Such sites also display a very significant level of bigotry in the way they define terms of service through discrimination and the way content and users are moderated. It often leaves one with a bad taste when such sites blur the perspectives and lose the whole defining objective of an open social web. Maybe, one way to avoid such sites is to not support their use until they are able to provide an equal and open approach for the way they handle users and content. In general, the collaborative approaches are not effective towards objectivity. They are also not efficient for establishing constructive and accurate ways towards intelligent solutions. For open question and answering as well as recommendations the approaches need to utilize semi-automated techniques to provide for better alternatives. They even could use a pure automated approach but that avoid social collaboration entirely. In a manner of speaking, social collaboration is only as good as when it enriches or adds value to an algorithm as part of human assistance, but they should be necessary in guiding an intelligent system towards a logical conclusion. Quora as a social platform does not work nor does it provide accurate answers. But, what it does do is provide variety of stagnating inputs in form of collaborative insightful answers which can be classed as opinions with very little added intelligence - not a very smart solution to an uncomplicated problem. Alternatively, they could provide an open question/answer search function alongside the community of answers that would add richer contextual value towards building a more natural semantics as well as discoverable analytics. An ontology could increase in harnessing more semantics as well. The natural step, in process, would be to spontaneously link the social web of discussions via linked data through which an evolving graph emerges.