Category: SOCIAL MEDIA

  • Snapchat Launches New Ad Campaign to Promote Connection in the App

    Snapchat Launches New Ad Campaign to Promote Connection in the App

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    As it looks to put more focus on growth in its top revenue markets, Snapchat’s launching another promotional campaign, designed to promote togetherness and community in the app.

    Snapchat’s latest “Less Likes. More Love” promotion is a continuation of its “Less Social Media. More Snapchat” push, which it launched back in February, as part of its broader effort to showcase the connective role that Snap plays in bringing people together.

    The key emphasis of the promotion is that Snapchat isn’t about broadcasting your life to strangers, but is more focused on connecting with friends, and maintaining relationships with your closest buddies.

    As per Snap:

    While we may look like social media to some, Snapchatters know we’re not. The experience of getting a Snap is different from getting a text or seeing a social post. Every new Snap connects you with a friend, where you can have fun and be wow-ed by each other’s creativity.”

    Which this new video underlines, via surreal means, and it does seem like an effective way to showcase the more enclosed community feel of the app, where you can share your less guarded, more playful moments.

    As noted, Snapchat’s making a bigger push to generate more attention, particularly in the North American market, in order to maximize its revenue opportunities. Snap’s maintained steady growth overall, and is now up to 422 million daily active users. But a lot of that growth, in recent times, has come from emerging markets, with India in particular seeing a big boost in Snap uptake.

    But Snap’s now putting more focus on building its U.S. and E.U. audiences, where it’s able to generate more ad revenue.

    It’s hard to say how impactful that will be, as you would assume that most of Snap’s target audience in these regions would be well aware of the app by now, but this is a good-looking promo, which could spark more interest, and get more people engaging in the app.

    And definitely, Snap’s efforts thus far have yielded results. In Q1, Snap posted a 21% year-over-year revenue increase.

    Maybe, then, promotions like this are significantly effective.

    Snap says the new promos will run online and on TV in the U.S. and U.K.

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  • TikTok Is Exploring a US Only Version of Its Feed Algorithm

    TikTok Is Exploring a US Only Version of Its Feed Algorithm

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    Would TikTok still be TikTok if it didn’t have its all-knowing algorithm feeding you more and more of the content that you want to watch every time you log in?

    That could be a key question of the next stage for the app, as it works to come up with alternatives to remain in operation in the U.S., after the U.S. Senate voted to force TikTok to be sold into U.S. ownership, or face a national ban, due to national security concerns.

    Chinese officials have reportedly already vetoed any potential sale of its algorithmic code, under China’s revised export-control rules, which stipulate that any sale involving its source code would require Government approval.

    Which means that a sale of TikTok as we know is unlikely, and now, according to reports, TikTok’s owner ByteDance is working to come up with another proposal.

    As reported by Reuters:

    TikTok is working on a clone of its recommendation algorithm for its 170 million U.S. users that may result in a version that operates independently of its Chinese parent and be more palatable to American lawmakers who want to ban it, according to sources with direct knowledge of the efforts.”

    According to the report, TikTok has been working on this alternative feed algorithm for more than a year, which was originally slated to be part of its broader “Project Texas” initiative designed to appease U.S. authorities.

    Which may be another path to TikTok remaining in operation in America, though I remain skeptical that it’s even possible to replicate TikTok’s algorithms in any lesser form, given the various parameters are qualifiers that are built into its system.

    Yet, at the same time, if any U.S. company was able to buy the whole app, algorithms and all, that could also be problematic, and lead to further queries and concerns from U.S. authorities.

    Back in 2020, an investigation found that TikTok had been advising its moderation teams to suppress uploads from users with physical “flaws” including “abnormal body shapes,” “ugly facial looks,” dwarfism, and “obvious beer belly,” among other traits.

    As reported by The Intercept:

    One moderation document outlining physical features, bodily and environmental elements deemed too unattractive spells out a litany of flaws that could be grounds for invisibly barring a given clip from the “For You” section of the app. The document reveals that uploads by unattractive, poor, or otherwise undesirable users could “decrease the short-term new user retention rate.”

    Wrinkles, eye disorders and various other “low quality” physical concerns were included in the censorship list, as well as videos created in poor shooting environments, like “slummy” style housing and “disreputable decorations.”

    TikTok management has repeatedly stated that these qualifiers were never used on TikTok itself, and had only ever been implemented in the Chinese version of the app, called Douyin. The advisory notes were merely ported over as templates, as part of TikTok’s global expansion, but a bigger concern that was largely overlooked at the time is that TikTok’s moderation team was only ever able to reject clips that depicted people who had these traits was because its visual identification process is so advanced that it’s able to highlight uploads in which such elements are potentially present in the first place.

    At 750 million users, there’s no way that Douyin’s moderation team would be able to filter every video upload in order to detect and reject those that fail on these parameters. The only effective process for removing videos that include these traits is via TikTok’s visual ID system, which points to the fact that a key element of TikTok’s addictive system algorithm is that it’s able to identify very specific physical traits of people in clips, along with other elements, in order to show you more of what you like.

    Which is a concern, for many reasons.

    If TikTok knows, for example, that you watched a video of a young, blonde girl with blue eyes dancing, it’ll show you more of that, down to more specifics than you probably expect.

    If it knows you like chunky guys with dark hair, guess what you’ll get shown more of? If it knows that you like seeing naked hips, you’ll get more of that.

    The depths of TikTok’s content matching, based on a broad range of visual traits, along with the regular text and topic cues, is why it’s so compelling, but it’s also why anyone taking on that algorithm needs to be aware that such specific matching will likely not be viewed favorably by U.S. authorities.

    TikTok has seemingly watered this down over time, while on Douyin, the Chinese Government also now plays a role in deciding what gains traction in the app.

    But there is a reason why TikTok’s algorithmic matching is so compelling, more so than U.S. apps. And I’m not sure that people really want to comprehend the actual answers.

    Which is also why a U.S.-only version of its algorithm won’t work, and would see TikTok lose ground very fast, if it is forced to enact a far more sanitized version of its systematic process.

    It may be null and void either way, because what I’m hearing from observers in China is that the forced TikTok sale has become a point of national pride, with the Chinese government opposing what it sees as overreach by U.S. authorities.

    As such, it seems increasingly likely that they’ll refuse any compromise, which will mean that, as of January next year, TikTok will be switched off for U.S. users.  

    So while discussions will continue on solutions, it may come down to international diplomacy, and a stand off between global superpowers.

    Yes, TikTok, the app that gained popularity on the back of viral dance trends, is now at the center of geopolitical tensions. What a time to be alive.  



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  • Snapchat Shares New Data on Ad Campaign Performance

    Snapchat Shares New Data on Ad Campaign Performance

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    Snapchat has published a new study into the variance in impact of branding and direct response campaigns, and which actually drives more direct sales activity in the app.

    The study, based on insights from TransUnion and Dentsu, examines how the varying focus of campaigns changes their eventual results, based on over three years of data from 36 advertisers across different verticals.

    The result?

    Although the share of spend between brand and DR was different per category, we found that across the board, there is no tradeoff, and both campaign strategies were efficient in driving sales. Both the brand and DR campaigns contribute disproportionately more sales relative to the share of budget each campaign type receives.”

    Snapchat Brand vs DR report

    As you can see in this chart, the relative performance of Snap campaigns for both brand and direct response is closely aligned, with the amount spent being the key driver of sales performance.

    As per Snap:

    One key takeaway from the research is that brand activity drives sales, especially on Snapchat. If we take a closer look at the Commerce category for example, when ROAS is split out between brand and DR campaigns, the research found that while both campaign types drive above average ROAS,  brand spend drove the higher ROAS. Brand spend driving above average ROAS was not unique just for the Commerce vertical. This is true across all the other categories in our research.

    Snapchat Brand vs DR report

    Snapchat also found that combining both branding and direct response campaigns drove even better response.

    “Across all of the verticals, the analysis revealed that running brand and DR campaigns concurrently delivers incremental ROAS compared to running in isolation to one another.

    Snapchat Brand vs DR report

    Essentially, while there’s a lot of ad industry jargon here, the study’s conclusion is that advertisers can drive greater campaign success on Snap by increasing their spend across both brand and direct response approaches, with the combination of the two helping to maximize reach and resonance, as well as direct sales.

    Which, given this is a study published by Snap itself, is no real surprise, but the data backs up the idea that Snap advertisers do see more sales activity when pushing both approaches, as opposed to focusing on one or the other.

    Some more food for thought for your Snap ads approach.

    You can read Snapchat’s full study overview here.

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  • X To Remove the Option To Hide Blue Tick

    X To Remove the Option To Hide Blue Tick

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    The fact that X offered this as an option in the first place is an indictment of its own product, while the fact that it’s now removing it is even less logical.

    But…

    X hide checkmark notification

    Yes, X (formerly Twitter) is notifying users who currently have a blue tick in the app that, soon, they’ll no longer have the option to hide it.

    So if you’re ashamed of your checkmark, and you don’t want people knowing, or thinking that you’re giving money to Elon to use his app, you’ll soon have no choice.

    Which is likely in response to X recently announcing that all users with 2,500 verified followers will get a free checkmark in the app.

    That saw a heap of prominent former Twitter users get their checkmarks back, despite not wanting them, and also not wanting to be seen as paying for the tick. So a lot of them just hid it, but now, X is taking that option away, meaning that more blue ticks will be displayed in-stream.

    So why would X remove the option?

    Well, X sees the blue checkmark as a signal of authenticity, and wants to use it, in part, as an anti-bot measure. Bots can’t pay for accounts, so in X’s view, all of the blue checkmark accounts are actual, real people.

    The more real people that have blue ticks, the more that the profiles without them stand out as potential bots, which reduces their standing in the app, and X is likely hoping that by having more checkmarks more visible, that’ll increase the pressure on non-subscribers to consider paying up to get on par with the rest.

    But that won’t work.

    Why? Because as X itself has noted, the vast majority of users (80%) never post or interact in the app, and view posts on X in “read only” mode. If you’re not posting anyway, why would you care if you have a checkmark or not, while X’s move to sell blue ticks has completely de-valued it as a symbol of status, which is why it had to add an option to hide the marker in the first place.

    In other words, X has undermined a key value proposition of its Premium subscription offering (the blue tick) by selling it to whomever is willing to pay. Which means that no one puts much stock in the marker anymore, so most users see absolutely no reason to pay to get it.

    Forcing more users to display the tick won’t help, as it’s just not worth anything anymore, and if you’re not paying for the other features of X Premium, the marker, in itself, is no longer the value add that it once may have been.

    Really, the whole push to reform verification has been a mess.

    X owner Elon Musk originally pledged to eradicate the “lords and peasants” system of verification in the app, by making blue ticks available to anyone, which he also saw as a path to generating a heap more revenue for the app.

    Indeed, in his original pitch to potential investors for his Twitter acquisition, Musk projected that, by the end of last year, X Premium would have 9 million subscribers, before rising to 104 million paying users by 2028. Musk also saw a path to the company generating $26.4 billion by 2028, with $10 billion of that coming from subscriptions.

    But none of that is even close to happening.

    Thus far, X Premium still has fewer than a million subscribers, or less than 0.5% of X’s total user base. At best, X Premium would be generating around $50 million per annum for the company, though it’s hard to know exactly how much it’s making due to variable Premium subscription pricing.

    Incentives like access to its Grok chatbot seemingly haven’t had a big impact, and without a more significant value-add, it’s hard to see how Musk and X will lure more subscribers.

    Peer pressure, through forced display of blue ticks, is unlikely to be a big element, while giving way X Premium to people with a lot of followers seems to reinstate the very “lords and peasants” system that Musk vowed to eliminate.

    But, I guess, it also needs to try something, especially with X’s overall revenue actually declining by half to $2.5 billion in 2023.

    Really, I don’t even see how X is going to stay in operation beyond the U.S. election.

    Advertisers are still seemingly hesitant to return to the app, which has significantly impacted its ad intake, while subscribers, as noted, are not even marginally close to Elon’s projections. And even with 80% fewer staff, X still has a lot of costs to cover, which also includes buying pricey GPUs to power its AI elements.

    As such, I’m not sure that its current intake is going to be sustainable for much longer.

    That’s likely why xAI is now seeking up to $4 billion in additional investment, and why X is pushing hard to lure ad partners back any way that it can.

    Maybe, if Elon splits out xAI and X as separate elements of X Corp, that’ll enable him to keep each running in isolation, without lumping its AI operational costs onto X itself, reducing X’s overheads.

    But even then, it’s hard to see how this all comes together as part of Musk’s “everything app” grand plan.

    As many have noted, Elon has been able to overcome seemingly impossible odds in the past, and has played a part in significant technological and industry shifts. But maybe, this time, he’s bitten off more than he can chew, and chewing faster is unlikely to be the way.



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  • TikTok Shares New Research on the Benefits of Creating Ads in Multiple Languages

    TikTok Shares New Research on the Benefits of Creating Ads in Multiple Languages

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    Given the projected rise in the Hispanic population in the U.S., it’s worth considering the language that you use in your ads, and whether you’re missing out on opportunities by failing to display your messages in multiple languages.

    That’s the key message of TikTok’s latest research report, conducted in partnership with NRG, which looked at how TikTok users respond to bilingual ads, and the approaches that brands can take to help boost their promotions across language barriers.

    As explained by TikTok:

    Bilingual audiences are the future of marketing on and off TikTok, as they are the drivers of US population growth. Already the youngest and largest ethnic group, Hispanics will become a third of the population by 2060. This growing population is highly engaged on social and digital platforms, utilizing them not just for personal connections but also as gateways to engaging with brands.”

    In line with this, TikTok suggests that brands should invest in bilingual ad development, in order to reach broader and more diverse audiences with their campaigns.

    “Brands can create long-lasting connections with this audience by incorporating Spanish language elements in ads, pushing beyond heritage months and speaking to their day-to-day lives year-round through authentic partnership with creators and celebrities from within their communities.

    TikTok bilingual ads

    TikTok’s research found that voiceovers had the greatest impact on upper- and mid-funnel metrics, including brand perception, connection, and consideration when compared to English-only ads.

    The research also shows that, with Spanish-speaking audiences in the U.S., ads that use a mix of languages resonate most with bilingual speakers.

    “An ad entirely in Spanish doesn’t speak to the dual sense of identity these audiences feel. Layering English and Spanish creative elements to create balance ensures that brands can speak to bilingual users in a way that feels true to their identity as well as appeal to a broader audience.

    TikTok bilingual ads

    Interestingly, the data also shows that English-speaking audiences appreciate brands that go to effort to be more inclusive by including other languages in their promotions.

    “When seeing ads that incorporated Spanish, Millennials were 1.6x more likely to say the brand cares about its customers and 1.5x more likely to feel that brand is trustworthy.”

    TikTok bilingual ads

    These are some interesting notes, and with the development of AI technology that will soon be able to replace the audio of content with alternative language translations (in sync with the speaker), you’ll soon have even more options to align with this, and create alternate or integrated versions of your promotions in various languages.

    It could become a major part of your planning, and TikTok’s data shows that it can drive broad benefits.

    You can check out the full report from TikTok here.

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  • Meta Updates AI Labeling Policy to Expose Generated Content in its Apps

    Meta Updates AI Labeling Policy to Expose Generated Content in its Apps

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    Meta’s updating its AI content labels to ensure that a broader range of synthetic content is being tagged, in response to a growing flood of generative AI posts in the app.

    Over the past few months, more and more AI engagement bait has been showing up on Facebook, and has also been generating a heap of engagement.

    Facebook AI post

    Like, even the slightest level of scrutiny would reveal that this is not a real image. But Facebook is used by billions of people, and not all of them are going to zoom in, or have the digital literacy to even be aware that generative AI can even be used in this way (as denoted by the 6.9k Likes on this post).

    As such, Meta’s updating its AI labeling policies to ensure that more AI-generated content is tagged and disclosed accordingly.

    As per Meta:

    Our existing approach is too narrow, since it only covers videos that are created or altered by AI to make a person appear to say something they didn’t say. Our manipulated media policy was written in 2020 when realistic AI-generated content was rare and the overarching concern was about videos. In the last four years, and particularly in the last year, people have developed other kinds of realistic AI-generated content like audio and photos, and this technology is quickly evolving.”

    The new process will see more “Made with AI” labels being appended to content when Meta detects “industry standard AI image indicators or when people disclose that they’re uploading AI-generated content”.

    Facebook AI labels

    Meta says that it will also leave more AI generative content up in its apps, as opposed to removing it, with the labels serving both an informational and educational purpose.

    If we determine that digitally-created or altered images, video or audio create a particularly high risk of materially deceiving the public on a matter of importance, we may add a more prominent label so people have more information and context. This overall approach gives people more information about the content so they can better assess it and so they will have context if they see the same content elsewhere.”

    In other words, the labels will immediately inform the user that the content is fake, while also showing what can now be done with AI, which will help to increase awareness of the same.

    It’s a good update, though a lot here does depend on Meta’s ability to be able to detect generative AI within posts.

    As noted, in the above example (and the many others like it), it’s very obvious to most that it’s been AI generated. But as AI systems improve, and people learn new ways to use them, these are also going to get harder to detect, which could limit Meta’s automated capacity to detect such.

    But then again, the new approach will also give Meta’s moderators more enforcement powers over the same, and it could very well serve an important purpose in raising awareness of what can be done with AI fakes. 

    Time will tell, but it does seem like a good update, which could have an impact on expanding AI use.

    Meta says that it will implement its new AI labeling process in May.

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  • X Expands Community Notes to India Ahead of Upcoming General Election

    X Expands Community Notes to India Ahead of Upcoming General Election

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    X (formerly Twitter) has announced another expansion of its Community Notes crowd-sourced fact-checking program, with users in India now able to sign-up to contribute notes in the app.

    Which is an important update, given that India is holding its general election in June, and the campaign around such is set to include various controversial debates, with X posts at the center of many such discussions.

    India is X’s third biggest region by users, with over 26 million people in the nation regularly turning to the app for news updates. The platform has also long been a critical connector for more controversial discussion, which has led the Indian Government to call on the company to ban certain users and conversations, in order to maintain order, and quell dissent.

    In this sense, India presents a difficult challenge for Elon Musk’s X project, not only because of the authoritarian measures taken by the Indian Government in regards to what Indian users can and cannot say in social apps, but also because Musk’s other business, Tesla, is seeking to build upon its business opportunities in the nation.

    Both Twitter and X have already clashed with the Indian Government over takedown requests, with the Modi regime calling on the company, at various times, to remove posts that it views as incendiary, and sparking unrest.

    Earlier this year, the Indian Government issued a new order for X to ban users that it had identified as “prompting civil disobedience” with their posts. X complied, but also noted that it disagreed with the request, and vowed to continue to challenge the Indian Government’s bans through whatever legal means available.

    Last year, X was also forced to remove a BBC documentary that was critical of Indian Prime Minister Narendra Modi after it was banned in the nation. Many critics of X used this as an example of the platform’s inability to uphold its own free speech ethos in the face of government pressure.  

    X, however, has always maintained that it’ll defend free speech within the realms of the law in each nation. And with the Indian Government dictating those rules, it is still aligning with this approach.

    But again, given Musk’s business interests, it will be interesting to see how X continues to address such, as opposing Indian authorities on one front could make things more complicated on the other.

    That hasn’t seemingly become a major point of contention as yet, and it might not ever emerge as a significant challenge.

    But in an election year, with the ruling government facing opposition on several fronts, things could come to a head, and Community Notes is unlikely to save Musk and Co. from expanded requests for censorship.



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  • Will AI Tools Take the ‘Social’ Out of ‘Social Media’?

    Will AI Tools Take the ‘Social’ Out of ‘Social Media’?

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    The growth of generative AI content has been rapid, and will continue to gain momentum as more web managers and publishers look to maximize optimization, and streamline productivity, via advanced digital tools.

    But what happens when AI content overtakes human input? What becomes of the internet when everything is just a copy of a copy of a digital likeness of actual human output?

    That’s the question many are now asking, as social platforms look to raise walls around their datasets, leaving AI start-ups scrambling for new inputs for their LLMs.

    X (formerly Twitter) for example has boosted the price of its API access, in order to restrict AI platforms from using X posts, as it develops its own “Grok” model based on the same. Meta has long limited API access, more so since the Cambridge Analytica disaster, and it’s also touting its unmatched data pool to fuel its Llama LLM.

    Google recently made a deal with Reddit to incorporate its data into its Gemini AI systems, and that’s another avenue you can expect to see more of, as social platforms that aren’t looking to build their own AI models seek new avenues for revenue through their insights.

    The Wall Street Journal reported today that OpenAI considered training its GPT-5 model on publicly available YouTube transcripts, amid concerns that the demand for valuable training data will outstrip supply within two years.

    It’s a significant problem, because while the new raft of AI tools are able to pump out human-like text, on virtually any topic, it’s not “intelligence” as such just yet. The current AI models use machine logic, and derivative assumption to place one word after another in sequence, based on human-created examples in their database. But these systems can’t think for themselves, and they don’t have any awareness of what the data they’re outputting means. It’s advanced math, in text and visual form, defined by a systematic logic.

    Which means that LLMs, and the AI tools built on them, at present at least, are not a replacement for human intelligence.

    That, of course, is the promise of “artificial general intelligence” (AGI), systems that can replicate the way that humans think, and come up with their own logic and reasoning to achieve defined tasks. Some suggest that this is not too from being a reality, but again, the systems that we can currently access are not anywhere close to what AGI could theoretically achieve.

    That’s also where many of the AI doomers are raising concerns, that once we do achieve a system that replicates a human brain, we could render ourselves obsolete, with a new, tech intelligence set to take over and become the dominant species on the earth.

    But most AI academics don’t believe that we’re close to that next breakthrough, despite what we’re seeing in the current wave of AI hype.

    Meta’s Chief AI scientist Yann LeCun discussed this notion recently on the Lex Friedman podcast, noting that we’re not yet close to AGI for a number of reasons:

    The first is that there is a number of characteristics of intelligent behavior. For example, the capacity to understand the world, understand the physical world, the ability to remember and retrieve things, persistent memory, the ability to reason and the ability to plan. Those are four essential characteristic of intelligent systems or entities, humans, animals. LLMs can do none of those, or they can only do them in a very primitive way.”

    LeCun says that the amount of data that humans intake is far beyond the limits of LLMs, which are reliant on human insights derived from the internet.

    “We see a lot more information than we glean from language, and despite our intuition, most of what we learn and most of our knowledge is through our observation and interaction with the real world, not through language.”

    In other words, its interactive capacity that’s the real key to learning, not replicating language. LLMs, in this sense, are advanced parrots, able to repeat what we’ve said back to us. But there’s no “brain” that can understand all the various human considerations behind that language.

    With this in mind, it’s a misnomer, in some ways, to even call these tools “intelligence”, and likely one of the contributors to the aforementioned AI conspiracies. The current tools require data on how we interact, in order to replicate it, but there’s no adaptive logic that understands what we mean when we pose questions to them.

    It’s doubtful that the current systems are even a step towards AGI in this respect, but more of a side note in broader development, but again, the key challenge that they now face is that as more web content gets churned through these systems, the actual outputs that we’re seeing are becoming less human, which looks set to be a key shift moving forward.

    Social platforms are making it easier and easier to augment your personality and insight with AI outputs, using advanced plagiarism to present yourself as something you’re not.

    Is that the future we want? Is that really an advance?

    In some ways, these systems will drive significant progress in discovery and process, but the side effect of systematic creation is that the color is being washed out of digital interaction, and we could potentially be left worse off as a result.

    In essence, what we’re likely to see is a dilution of human interaction, to the point where we’ll need to question everything. Which will push more people away from public posting, and further into enclosed, private chats, where you know and trust the other participants.

    In other words, the race to incorporate what’s currently being described as “AI” could end up being a net negative, and could see the “social” part of “social media” undermined entirely.

    Which will leave less and less human input for LLMs over time, and erode the very foundation of such systems.

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  • Instagram Tests Blend Feed for Private Content Sharing

    Instagram Tests Blend Feed for Private Content Sharing

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    Instagram continues to test out more private engagement options, this time via a new option called “Blend”, which is essentially a combined Reels feed, based on the Reels that you’ve shared with a friend.

    Instagram Blend

    As you can see in this example, shared by app researcher Alessandro Paluzzi, your combined “Blend” stream, with a friend/connection, will showcase:

    “Reels recommendations based on Reels you’ve shared with each other and your Reels interests”.

    So essentially, Instagram’s trying out a more integrated way to facilitate Reels sharing, by tapping into the user behavior of sharing Reels via DM.

    Which is indeed becoming a much more common practice.

    Back in 2022, Instagram chief Adam Mosseri noted:

    Friends now post a lot more to stories, and send a lot more DMs, than they post to Feed.”

    Based on this, Instagram has been working on a range of new features to tap into private sharing in the app, including:

    • Inbox Notes, a conversation-prompting option which highlights chat prompts from your connections at the top of your DM inbox
    • Channels, providing a one-to-many messaging option, aimed at celebrities and creators looking for a DM-aligned way to stay in touch with fans
    • Collections, which enables users to collaborate and interact around selected posts shared in a group feed
    • Instagram’s also added a new option to share feed posts with Close Friends only

    Blend seems like a more targeted approach to link into the usage of Instagram as a content aggregator, with more and more people sharing their favorite memes and clips with friends, either via DM or in real life.

    The option could make this a more engaging, in-app element, where actual content discovery is also a group experience, and that could actually be a valuable addition.

    Though it also might not work.

    Instagram isn’t live testing this as yet, but if it can get it right, it does seem like it could have potential.



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  • YouTube Adds Improved Audience Retention Data, Streamlined Pre-Checks

    YouTube Adds Improved Audience Retention Data, Streamlined Pre-Checks

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    YouTube adding even more analytics tools, providing additional avenues to compare your content performance, while it’s also rolling out a new channel display customization option, as well as streamlined pre-upload checks.

    First off, YouTube’s adding audience segments into its “Audience Retention” report, which will enable channel managers to compare viewer engagement for different audience groups.

    YouTube audience analytics

    As you can see in this example, now, you’ll be able to compare, say, “New” and “Returning” viewers and how they’re engaging with your content, which will give you more ways to refine your approach based on each group.

    You’ll also be able to see “Subscribed” versus “Non-subscribed” viewer retention, which could be helpful in understanding what’s driving subscriber growth.

    It’s the latest in YouTube’s ever-advancing analytics suite, to which it’s already announced several key additions in the first three months of this year.

    YouTube says that it’s rolling out the feature from this week. It’ll be available in YouTube Studio analytics within the “Advanced” mode.

    On another front, YouTube’s also making its “For You” shelf on channels customizable, which will enable you to add or remove videos from the display, specify what videos you want to be shown, reposition the display, etc.

    YouTube audience analytics

    The “For You” listing displays the content from your channel that’s likely to be of most interest to each visitor, but by managing which videos it can choose from, that could be another way to guide viewers towards more specific content and content types.

    That does come with some risk, in that users may not be shown the most engaging post, but it’s another consideration for channel management.

    Finally, YouTube’s also looking to improve its video pre-check process, by consolidating all pre-check notifications to the central notification settings.

    As per YouTube:

    “Monetizing creators who upload a video can be notified when their pre-check is complete. To do this, visit Studio mobile, tap your profile picture, tap “Settings”, tap “Push Notifications”, and select the “Policy” toggle button. Now, you can step away, and when the pre-check process is complete, you’ll be notified via Studio.”

    That’ll make it easier to manage the upload process, and ensure your videos are eligible for monetization.

    It’s another set of helpful updates for YouTube creators, which will make it easier to maximize your content performance.

    You can learn more about YouTube’s latest updates here.

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