Introduction

Streaming manipulation refers to activities that distort music consumption, measurement or monetisation on digital streaming platforms. It includes practices that do not reflect genuine listener engagement or demand, and which are intended to influence streaming statistics, visibility and the allocation of revenues across rightsholders, ultimately diverting revenues away from legitimate artists and labels. AI has exacerbated these practices, both by enabling increasingly sophisticated automated bot activity and by making it easier to generate volumes of content that can be used to support and monetise such manipulation, as illustrated by the Michael Smith case in the US.

The case also demonstrates that such practices amount to theft, which costs the music industry an estimated $2bn a year and causes long-term damage for labels and artists.

Streaming manipulation can broadly be divided into demand-side manipulation and supply-side manipulation. Demand-side manipulation involves artificially generating or directing listening activity that does not reflect genuine listener intent. Supply-side manipulation involves the creation, use, or exploitation of content in ways designed to distort the streaming ecosystem, divert revenues, or otherwise influence platform outcomes. While these categories may overlap in practice, distinguishing between them helps clarify the different forms of harm involved and the measures needed to address them.

It is difficult to assess the full scale of streaming manipulation, as definitions vary across platforms and only detected activity can be measured. Industry estimates suggest that a meaningful share of global streams may be associated with that, with Beatdapp indicating figures of around 10% of total streams although differences in detection systems and the rapidly evolving nature of manipulation tactics make this uncertain.

Streaming manipulation was often concentrated around new releases[1], where artificial activity was used to inflate early momentum. It is now increasingly distributed across the full lifecycle of a track or album and increasingly represents flooding strategies at market level[2].

When it comes to tackling this issue, the whole music industry has an important role to play. In 2019, streaming services and music industry representatives signed a voluntary Code of best practice on streaming manipulation. We seek an urgent update to this code as it provides a strategic framework for addressing the issue across the sector and remains the only pan-industry initiative of its kind. It demonstrates that, despite the diversity and fragmentation of the music industry, stakeholders can come together to address shared challenges. Since then, other initiatives, such as the Music Fights Fraud Alliance have been launched and play an important operational role in supporting the detection and prevention of streaming manipulation. Cooperation across the market remains crucial to address this issue.

IMPALA also condemns other forms of streaming manipulation, including the creation of fake or misleading artist profiles, the impersonation of legitimate artists, and the unauthorised upload or re-upload of existing recordings under different names or in modified forms. Such practices distort the market, undermine trust and, where protected recordings are used without authorisation, constitute copyright infringement.

In this note, IMPALA attempts to draw attention to manipulation tactics and how to detect them, including abnormal data patterns and red flags, as well as possible actions to take in response. Tactics will continue to evolve, and no single indicator is enough to confirm manipulation. It is therefore important to continuously monitor streaming data and consider the broader set of signals when assessing potential manipulation.

[1] Due to the commercial and algorithmic sensitivity of early streaming activity in digital music services which can affect chart eligibility, editorial and algorithmic playlist consideration, and recommendation system visibility.

[2] In April 2026, Deezer announced that around 75,000 fully AI-generated tracks are uploaded to the platform every day, accounting for approximately 44% of total daily uploads. Although they currently represent only a small share of total streams, this illustrates how quickly AI-generated content is scaling across streaming catalogues, increasing the volume of material circulating within the ecosystem.

The case also demonstrates that such practices amount to theft, which costs the music industry an estimated $2bn a year and causes long-term damage for labels and artists.

Streaming manipulation can broadly be divided into demand-side manipulation and supply-side manipulation. Demand-side manipulation involves artificially generating or directing listening activity that does not reflect genuine listener intent. Supply-side manipulation involves the creation, use, or exploitation of content in ways designed to distort the streaming ecosystem, divert revenues, or otherwise influence platform outcomes. While these categories may overlap in practice, distinguishing between them helps clarify the different forms of harm involved and the measures needed to address them.

It is difficult to assess the full scale of streaming manipulation, as definitions vary across platforms and only detected activity can be measured. Industry estimates suggest that a meaningful share of global streams may be associated with that, with Beatdapp indicating figures of around 10% of total streams although differences in detection systems and the rapidly evolving nature of manipulation tactics make this uncertain.

Streaming manipulation was often concentrated around new releases , where artificial activity was used to inflate early momentum. It is now increasingly distributed across the full lifecycle of a track or album and increasingly represents flooding strategies at market level. 

When it comes to tackling this issue, the whole music industry has an important role to play. In 2019, streaming services and music industry representatives signed a voluntary Code of best practice on streaming manipulation. We seek an urgent update to this code as it provides a strategic framework for addressing the issue across the sector and remains the only pan-industry initiative of its kind. It demonstrates that, despite the diversity and fragmentation of the music industry, stakeholders can come together to address shared challenges. Since then, other initiatives, such as the Music Fights Fraud Alliance have been launched and play an important operational role in supporting the detection and prevention of streaming manipulation. Cooperation across the market remains crucial to address this issue.

IMPALA also condemns other forms of streaming manipulation, including the creation of fake or misleading artist profiles, the impersonation of legitimate artists, and the unauthorised upload or re-upload of existing recordings under different names or in modified forms. Such practices distort the market, undermine trust and, where protected recordings are used without authorisation, constitute copyright infringement.

In this note, IMPALA attempts to draw attention to manipulation tactics and how to detect them, including abnormal data patterns and red flags, as well as possible actions to take in response. Tactics will continue to evolve, and no single indicator is enough to confirm manipulation. It is therefore important to continuously monitor streaming data and consider the broader set of signals when assessing potential manipulation.

I. Demand-Side Manipulation

Demand-side manipulation refers to practices which are largely referred to as artificial streaming. In this context, three broad categories of actors can often be identified:

• criminal or fraudulent operators who organise and profit from manipulation schemes;
• artists, labels or rights holders who purchase promotional services without necessarily understanding that those services rely on manipulation techniques; and
• artists and labels who become victims of manipulation when their catalogues, profiles or recordings are used without their knowledge as part of fraudulent activity.

Again, distinguishing between these groups is important when considering appropriate responses, enforcement measures and support mechanisms.

What is artificial streaming?

Artificial streaming refers to activities that inflate stream counts without reflecting genuine listening and constitutes the most common form of demand-side manipulation. Artificial streams can be generated through:

• Hacking of existing user accounts or creation of fake
• Bots, scripts and other automated processes designed to manipulate streaming activity
• Coordinated streaming farms or click farms using networks of accounts or devices to generate streams at scale.
• Third-party promotional services that offer guaranteed streams, playlist placements, or rapid audience growth, and which may rely on undisclosed or artificial methods

These methods are often used in combination as part of layered systems of manipulation, rather than as isolated actions. Manipulation activity may also begin before music reaches streaming services. Artificial engagement on social media platforms (eg TikTok), including the use of bots, fake accounts or coordinated engagement campaigns, can be used to direct traffic towards streaming services and create the appearance of audience demand.

Some artists and labels may engage with third-party promotional services believing they are receiving legitimate marketing support. However, certain services that promise guaranteed performance outcomes may rely on artificial streaming techniques. This can expose artists and labels—sometimes unknowingly—to risks such as financial penalties, content takedowns, reputational damage, and loss of trust in audience data.

These practices are increasingly enabled and scaled by automation technologies, including AI and generative tools.

Why is artificial streaming bad for an artist/label?

Artificial streaming can temporarily inflate the streaming statistics of a track or artist, creating the impression of stronger performance than is actually the case. However, streaming services actively detect and remove artificial streams. This can lead to later adjustments in reported figures and sudden drops in plays and audience numbers.

• It is important to recognise that artists and labels may be affected by artificial streaming without having initiated or authorised it. Fraudulent operators may target legitimate catalogues, artist profiles or recordings as part of broader manipulation schemes. Enforcement measures should therefore distinguish, wherever possible, between bad actors and victims of manipulation.
• It also distorts how revenue is distributed. Because artificial streams do not reflect genuine listener demand, they do not contribute to real audience growth. This can result in an unfair redistribution of income between rightsholders. This undermines trust in streaming data and damages confidence in the wider music ecosystem.
• Artificial streaming can also trigger enforcement measures from streaming services and distributors, from content removal to restrictions on releases, or financial penalties depending on platform policies[1]. It can also have broader commercial and reputational consequences. Tracks or artists associated with artificial streaming risk being excluded from charts or editorial opportunities and may see their eligibility for certain promotional tools affected. Algorithmic recommendation systems can also be distorted by artificial activity, reducing the accuracy of recommendations and weakening long-term visibility with real audiences. Accurate fan data is now crucial for delivering and assessing effective marketing campaigns and building successful artist businesses, and if stream manipulation is employed it becomes impossible to distinguish between actual fan data and artificial fan This substantially decreases the value and reliability of streaming analytics.
• An analysis conducted within IMPALA’s Digital Committee illustrates how an artificial streaming campaign can operate in practice. In the end, the streams generated through this campaign were identified and removed by Spotify as part of their anti-streaming manipulation policies. And given the new rules on the platform regarding streaming manipulation, this initiative would now trigger a fine to be paid by the distributor of the label who made the test.  

• The analysis also highlights a number of red flags that can help identify whether a track is generating artificial streams, and these should be carefully monitored in light of the risks described above. This is the focus of our next section.