Jennifer Garner, Jennifer Lopez clashing over major Ben Affleck problem.

[ 10/12/2023 09:03 ] Read on thenews.com.pk

Report Overview: The article contains biased language, sensationalism, and cherry-picking data. It presents the issue as a clash between Jennifer Garner and Jennifer Lopez over Ben Affleck's smoking habit, using words like 'clashing' and 'disgusting' to create drama. The article also selectively quotes sources to support the narrative of tension between the two women. To improve objectivity, the article should provide a more balanced perspective and avoid using biased language.

Sides Objectivity Scores

Jennifer Garner (50)

Jennifer Lopez (50)

Sides Representation Balance

Favored Side

None

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Detected Manipulations & Suggested Changes

Biased language

The article uses biased language to create a negative perception of Jennifer Lopez and a sympathetic view of Jennifer Garner.

Words like 'clashing,' 'disgusting,' and 'infuriates' are used to portray Jennifer Lopez in a negative light and evoke strong emotions.

Use neutral language to present the differing views of Jennifer Garner and Jennifer Lopez.

Sensationalism

The article uses sensational language to create drama and attract attention.

Phrases like 'clashing over major Ben Affleck problem' and 'putting her patience to the test' are used to exaggerate the situation.

Use more neutral and factual language to describe the situation.

Cherry-picking data

The article selectively presents information to support a particular narrative.

The article focuses on the disagreement between Jennifer Garner and Jennifer Lopez over Ben Affleck's smoking habit, ignoring other aspects of their relationship.

Provide a more comprehensive view of the situation by including other relevant information.

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- This is an EXPERIMENTAL DEMO version that is not intended to be used for any other purpose than to showcase the technology's potential. We are in the process of developing more sophisticated algorithms to significantly enhance the reliability and consistency of evaluations. Nevertheless, even in its current state, HonestyMeter frequently offers valuable insights that are challenging for humans to detect.