When ‘IP address: xxx’ becomes a weapon of regional discrimination

Introduction

Since 2022, Chinese social media platforms have been required to display users’ IP locations under the Internet User Account Information Management Regulations. Posts and comments now show provincial locations for domestic users and country/region labels for those outside China (Zhou, Wei and Liao, 2024).

Officially, this policy aims to ‘combat fake accounts and enhance accountability’. Sounds reasonable, right? But in practice, it’s taken a messy turn. Scroll through any comment section now, and you’ll find gems like ‘All the defenders here have Henan Ips’ or ‘Jingye, prove your wealth by V me 50 yuan (typically means transfer 50 yuan to me via WeChat)’. The latter one is a meme mocking Beijingers’ wealth or perceived superiority—often with a sarcastic twist. When users’ posts are stamped with labels like ‘IP Location: Henan’ or ‘IP Location: Beijing’, these neutral tags quickly morph into weapons for identity-based attacks. It’s turned into a new form of regional discrimination.

Now, don’t get me wrong—I’m all for cleaning up toxic behavior online. But forcing everyone to show their IP is like putting shackles on every pedestrian just to catch a few pickpockets. It might help in theory, but in practice, it’s the regular people who get hurt first. Hate speech now requires zero creativity: just take one look at someone’s IP and launch into a regional slur.

To be fair, I can see how this ‘transparency in exchange for safety’ idea has some benefits. It did help expose fake influencers and paid troll armies. But the problem is, we gave up part of our privacy and didn’t get a cleaner internet in return—instead, we got a new set of problems. It’s like paying protection money to a gang, only to have them memorize your address.

So what I want to talk about is this: while the policy claims to target disinformation, it might actually be fueling more hate online through regional labeling. It’s time we reflect on the ethical risks of this kind of tech-based governance.

IP Labels Are Turning Real People into Stereotypes

Imagine this: you’re at the supermarket shopping for apples. Every apple on the shelf comes with a big sticker—‘Sweet from Shandong’ or ‘Sour from Shaanxi’. But here’s the thing: Shandong grows sour apples too, and Shanxi has sweet ones. Still, once the label’s there, you just grab a Shandong apple blindfolded.

Social media’s IP labeling system works the same way.

A user from Henan shares a thoughtful book review, only to be bombarded in the comments with ‘A manhole cover thief pretending to be cultured?’ Another ‘funnier’ example: someone with a Yunnan IP says that ever since platforms started showing IP locations, regional discrimination online has decreased. The reply? ‘Are you hallucinating from eating too many poisonous mushrooms?’ (For context: Yunnan is known for its wild mushrooms, some of which are toxic and hallucinogenic.) It has become a running joke—even though regional hate is no joke. These people aren’t responding to your ideas; they’re reacting to where you’re ‘produced’.

What’s worse is that these stereotypes feed themselves. When algorithms learn that ‘Henan = manhole theft’ or ‘Beijing = privilege’ drives engagement, they push these tropes to more feeds. Over time, even Henan users start doubting: ‘Am I really lesser?’

Case Study: The ‘Jingye’ Attacks on Beijing IPs—How Regional Bias Masks Deeper Issues

Take one of the most typical examples—the ‘Jingye’ (京爷) meme, also known as the ‘Beijing masters’ stereotype.

Someone with a Beijing IP posts a totally ordinary update, like “Working overtime again. Exhausted.” Instantly, the comments turn sarcastic like ‘Overtime? You’re from Beijing, don’t you have ten properties bringing in rent?’ or ‘Jingye V me 50 (meaning ‘Beijing boss, show me your 50 yuan power)’. But the reality might be completely different—this person could be a migrant worker in Beijing, sharing a flat with others, working late into the night, commuting home at 10 PM via packed metros. Their IP location? It might just simply reflect their office network.

Even if they are local Beijingers, they might be living in a run-down apartment in an old hutong neighborhood, far from the so-called ‘privileged class’.

But nobody really cares about that. A Beijing IP tag alone brands you as ‘Jingye’—a term mixing envy and resentment. The ‘Jingye v50’ meme might seem playful and harmless, but its subtext is vicious: ‘You Beijingers just sit back and collect rent thanks to your household registration and property. Who are you to complain?’

This ties into what Sinpeng and others calls the ‘deprivation of powers’ category of hate speech (Sinpeng et al., 2021). ‘Privileged people don’t get to whine,’ the logic goes. Even if you are earning a modest salary, renting a tiny flat, and facing daily hardships, as long as your IP shows Beijing, people treat you like you’ve lost the right to feel tired.

Regional Bias: When Online Attacks Become Venting Grounds for Real-World Grievances

Let’s take confronting examples.

Beijing has the highest density of top-tier hospitals in China, while some counties in Henan Province lack even basic CT scanners. When a Beijing IP user complains about ‘struggling to book a specialist appointment,’ someone from Henan might snap: ‘At least you can book one—we can’t even get past the hospital gates!’ It’s that sense of disparity that fuels the attacks.

Beijing also hosts the headquarters of the biggest corporations. Millions of migrant workers live and work in the city, paying taxes, contributing to the system, just like locals. But when it comes to school enrolment, they’re required to present five different certificates. When they’re sick, they often have to travel back to their hometowns to claim medical reimbursements. These are the people most wronged by ‘Jingye’ taunts—they know better than anyone that a Beijing IP ≠ a Beijing hukou (household registration), and a Beijing hukou ≠ automatic privilege.

But anger needs an outlet. Behind these attacks lie two twisted emotions:

First, anger at monopolized resources. Beijing has better education, healthcare, and policies than anywhere else. Outsiders go to great lengths just to squeeze in, and getting a Beijing household registration feels near impossible. So when they see a Beijing IP, it becomes a symbol of ‘the people who already have everything’. The result? A twisted logic that ‘If I can’t have it, neither should you’.

Second, a kind of sour grapes mentality. ‘Letting off a bit of steam by mocking you makes me feel a little more balanced.’

But here’s the thing—this anger is often aimed at the wrong people. The actual issue is the system that distributes resources so unequally—not a random person with a Beijing IP. Instead of directing criticism toward the mechanisms that need fixing, people lash out at individuals who are just as caught in the struggle.

The irony is, none of this helps solve the actual problems. The gap between rural and urban areas is still there. Resources are still distributed unevenly. But once the insults start flying, everyone’s attention gets diverted. Real issues get drowned out by the noise of regional hate. Who’s still in the mood to have a proper conversation about real problems?

So Why Aren’t Platforms Cracking Down on Regional Hate? What’s Up With the Moderation?

Here’s the big question—don’t platforms have moderation systems? Why are they failing to stop this wave of regional hate?

Let’s take an example from Facebook. In Myanmar, Facebook’s algorithm didn’t understand the word kalar—which on the surface just meant ‘foreigner’, but in context was a racist slur against the Rohingya. Because the platform failed to catch the nuance, hate speech spread unchecked, eventually contributing to large-scale violence (Sinpeng et al., 2021).

China’s regional bias problem follows the same playbook:

Algorithms miss cultural context. Platforms treat terms like ‘Jingye’ as harmless memes, ignoring the sarcastic tone or derogatory subtext—just like Facebook overlooked the malice behind ‘kalar’ (Sinpeng et al., 2021). The result is that — as Flew mentioned the problem of ‘algorithmic bias’ (Flew, 2021) — offensive content is retained for a long time and even recommended to more people by the algorithm.

When the targets of these attacks report the abuse, they often receive a response like ‘No violation found’. That response creates a deep sense of disappointment in the moderation process—‘reporting fatigue’ (Sinpeng et al., 2021). If speaking up does nothing, why bother? Silence sets in, and the cycle spins on.

What Platforms Can Do: Using IP Labels to Fight Back Against Regional Hate

Since removing the mandatory IP display isn’t up to platforms, here’s a practical idea from the platform’s point of view: use the IP data not to fuel hate, but to fight it. The data itself can be a powerful tool—if used well, it can expose targeted attacks and help prevent them. Here’s how, in three steps:

Step 1: Tag Every Comment With a ‘Geographic Fingerprint’

The platform binds the IP address of each piece of content to the text content to form structured data. For example, a comment ‘People from A province are of really poor quality’ + ‘IP address B’.

Step 2: Unmask Coordinated Attacks

With enough geo-tagged data, platforms can do two critical things:

Spot sudden spikes: If negative comments mentioning ‘Henan’ surge by 10x in a day, trigger an alert—it could be event-driven hate or organized trolling.

Map cross-province feuds: Detect patterns like ‘90% of attacks on Jiangsu users come from Zhejiang Ips’. Facebook’s Myanmar failure happened precisely because it missed such clusters—Buddhist-majority IPs targeting Muslim communities.

Step 3: Surgical Strikes, Not Blanket Bans

Once problems are flagged, platforms need precision:

human review firstly: Send obvious attacks to human moderators first. No letting AI go wild deleting anything.

Educate the AI: ‘Holland’ isn’t the Netherlands—it’s a slur for Henan. ‘Sweden’ mocks Northeast China. Learn from Weibo’s playbook: When users replaced ‘idiot’ with ‘paratrooper’ (伞兵, a homophone), the platform added it to banned terms.

Mute serial offenders: For accounts attacking the same region repeatedly (e.g., 20 hate comments/day), collapse their replies behind a ‘Click to view’ wall.

But don’t overdo it! Over-reliance on location tags could censor legitimate discussions. A post like ‘Northeast China needs revitalization policies’ isn’t hate speech—it’s constructive criticism. This is where human moderators matter most.

Conclusion: When Tech Governance Meets Human Bias

China’s IP location disclosure was meant to fight disinformation and boost transparency. But instead, it turned into a tool for regional discrimination. A neutral label became a trigger for hate, exposing a deeper truth: in the digital age, even rational tech governance can be overpowered by irrational human prejudice.

From the mockery of ‘Jingye v50’ to ‘Henan manhole thieves’, the essence of regional attacks is the online projection of uneven resource distribution. The reason why Beijing IP addresses are targeted is because they are simplified as symbols of the ‘privileged class’, while the real-life survival difficulties of Beijing migrant workers, and ordinary citizens with Beijing IP addresses are selectively ignored.

To break out of this dilemma, platforms need smarter strategies. First, it must be acknowledged that data can be a weapon or an antidote. By binding IP location to content analysis, platforms can more quickly identify ‘gang operations’ of regional attacks (for example, users in a certain province concentrating on attacking another province), and then prioritize manual review instead of relying on one-size-fits-all algorithms. At the same time, simply designing interventions (such as labeling ‘IP location does not represent identity or personal value’ next to the location) may be more effective in curbing the growth of prejudice than banning 10,000 accounts.

Platforms, users, and policymakers should all understand that IP address display is not the end, but the beginning of reflection. We can use it to identify problems, but we must never let it become a tool for creating new problems.

References

Flew, T. (2021). Regulating Platforms. Polity Press.

Sinpeng, A., Martin, F. R., Gelber, K., & Shields, K. (2021). Facebook: Regulating Hate Speech in the Asia Pacific. Ses.library.usyd.edu.au. https://doi.org/10.25910/j09v-sq57

Zhou, M., Wei, Z., & Liao, J. (2024). How Can the Universal Disclosure of Provincial-level IP Geolocation Change the Landscape of Social Media Analysis. ACM SIGWEB Newsletter, 2024(Autumn), 1–9. https://doi.org/10.1145/3704991.3704994