the politics of news personalization · the politics of news personalization lin hu 1 anqi li 2...

The Politics of News Personalization

Lin Hu 1 Anqi Li 2 Ilya Segal 3

1Australian National University

2Washington University in St. Louis

3Stanford University

October 2019

Changing News Landscape

Increasing online news consumption via social media and mobiledevices:

In 2016, 40% Americans frequently consulted online newssources, 62% got news on social media and 18% did so oftenIn 2017, 85% U.S. adults got news on mobile devicesIn 2018, social media outpaced print newspapers as a newssource

Rise of News Aggregators

Aggregator sites, social media feeds, mobile news apps:Gather tons of users’ personal data (demographic attributes,digital footprints, social network positions)Personalized news aggregation in exchange for user attention

Use and impact:Google News aggregated contents from more than 25,000publishers in 2013The top 3 popular news websites in 2019: Yahoo! News,Google News and Huffington Post, are aggregatorsSocial media feeds in 2016 U.S. presidential election

Potential Impact on Politics

For too many of us, it’s become safer to retreat into our ownbubbles, ...especially our social media feeds, surrounded by peoplewho look like us and share the same political outlook and neverchallenge our assumptions... And increasingly, we become sosecure in our bubbles that we start accepting only information,whether it’s true or not, that fits our opinions, instead of basingour opinions on the evidence that is out there.

—Barack Obama, farewell address, January 10, 2017

Research Questions

What kind of personalized news is aggregated for and consumed byrational inattentive voters in equilibrium?

How does news personalization affect policy polarization in amodel of electoral competition?

Agenda

1. ModelNews aggregationElectoral competition

2. Extensions3. Literature

Agenda

1. ModelNews aggregation

SetupOptimal news signal

Electoral competition2. Extensions3. Literature

Political Players

Two candidates L and R:Office-motivatedPolicy space: A = [−a, a]Policy profile: a = 〈aL, aR〉, fixed to any 〈−a, a〉, a ≥ 0 fornow

A unit mass of voters:Types: K = −1, 0, 1Population function: q : K → R+, q (−k) = q (k)Valuation of policies: u (a, k) = −|t(k)− a|, t : K → R isstrictly increasing and t (k) = −t (−k)

Expressive Voting

Utility difference from choosing candidate R over candidate L:

v (a, k) + ω

wherev (a, k) = u (aR , k)− u (aL, k)ω: valence state about fitness for office:

E.g., whether the state favors experience with the use of hardor soft powerEqual ±1 with prob. .5

News Aggregation

A monopolistic infomediary partitions K into market segmentsusing segmentation technology S:

Broadcast news: b = KPersonalized news: p = k : k ∈ K

Aggregates ω into |S| news signals, one for each market segment

A news signal Π : Ω→ ∆ (Z) is a finite signal structure:Z: set of news realizationsΠ (· | ω): probability distribution over Z conditional on thestate being ω

News Consumption

Each voter can either consume the news signal offered to him orabstain

Consume news = absorb the information contained in the newssignal:

Potential gain from improved expressive votingAttention cost: λ · I (Π)

Infomediary’s gross profit = total amount of attention paid byvoters

Model Discussion: News Signal

Under signal structure Π : Ω→ ∆ (Z),πz : prob. that the news realization is zµz : posterior mean of the state given news realization z

Strictly prefer candidate R to L iff v (a, k) + µz > 0—————– candidate L to R iff v (a, k) + µz < 0

Bayes’ plausibility: ∑z∈Z

πz · µz = 0

The infomediary can commit to any signal structure

Model Discussion: Attention Cost

Assumption 1.The needed attention level for consuming Π : Ω→ ∆ (Z) is

I (Π) =∑z∈Z

πz · h (µz) ,

where h : [−1, 1]→ R+ satisfies the following properties:(i) h (0) = 0 and strict convexity;(ii) continuity on [−1, 1] and twice differentiability on (−1, 1);

(iii) symmetry around zero.

E.g., h (µ) = µ2; h(µ) = H(

1+µ2

), H = binary entropy function

Model Discussion: Miscellaneous

Voter’s inflexibilityAttention-based business modelAbility to personalize

Agenda

1. ModelNews aggregation

SetupOptimal news signal

Electoral competition2. Extensions3. Literature

Optimal News Signal

Expected utility gain from news consumption:

V (Π; a, k) =

∑z∈Z

πz [v (a, k) + µz ]+ if k ≤ 0

−∑z∈Z

πz [v (a, k) + µz ]− if k > 0

Under segmentation technology S, any optimal news signal ofmarket segment s ∈ S solves

maxΠ

I (Π) ·

∑k∈K:V (Π;a,k)≥λ·I(Π)

q (k, s)

(s)

Binary Recommendations and Strict Obedience

For binary news signals, write Z = L,R and assume w.l.o.g.that µL < 0 < µR

A binary news signal induces strict obedience if the followingholds among its consumers:

v (a, k) + µL < 0 < v (a, k) + µR (SOB)

Binary Recommendations and Strict Obedience (Cont’d)

Lemma 1.Fix any symmetric policy profile 〈−a, a〉, a ≥ 0 and assumeAssumption 1. Then,

(i) any optimal broadcast news signal is either degenerate orbinary;

(ii) any optimal personalized news signal of any type of voters iseither degenerate or binary;

(iii) any optimal news signal, if binary, induces strict obedience.

Uniqueness

Lemma 2.Fix any symmetric policy profile 〈−a, a〉, a ≥ 0 and assumeAssumption 1. Then,

(i) in the broadcast case, if it is optimal to induce consumptionfrom all voters, then the optimal news signal is unique;

(ii) the optimal personalized news signal of any type of voters isunique.

Regularity Condition

Assumption 2.Under any symmetric policy profile 〈−a, a〉, a ≥ 0,

(i) any optimal news signal is nondegenerate, and the posteriormeans of the state conditional on its realizations belong to theopen interval (−1, 1);

(ii) it is optimal to induce consumption from all voters in thebroadcast case.

Notations

Under segmentation technology S:ΠS (a, k): optimal news signal consumed by type k votersµSz (a, k): the posterior mean of the state given newsrealization z ∈ L,R

πS (a, k) = − µSL (a,k)

µSR (a,k)−µS

L (a,k) : prob. that candidate R isendorsed

Suppress the notation of k if S = b

Own-Party Bias and Occasional Big Surprise

News signal (B)

News signal for k<0 (P)

News signal for k=0 (P)

News signal for k>0 (P)

-1 -0.8 -0.6 -0.4 -0.2 00

0.2

0.4

0.6

0.8

1

μL

μR

Figure 1: Optimal news signals

Own-Party Bias and Occasional Big Surprise (Cont’d)

Theorem 1.Fix any symmetric policy profile 〈−a, a〉, a ≥ 0 and assumeAssumptions 1 and 2. Then,

(i) πb (a) = 1/2 and µbL (a) + µb

R (a) = 0;(ii) ∀k ∈ K, µp

L (a,−k) + µpR (a, k) = 0, and

(a) πp (a, k) < 1/2 and µpL (a, k) + µp

R (a, k) > 0 if k < 0;(b) πp (a, k) = 1/2 and µp

L (a, k) + µpR (a, k) = 0 if k = 0;

(c) πp (a, k) > 1/2 and µpL (a, k) + µp

R (a, k) < 0 if k > 0;

(iii) I (Πp (a, k)) > I(

Πb (a))∀k ∈ K.

Agenda

1. ModelNews personalizationElectoral Competition


Game Sequence

1. The infomediary commits to news signals2. a Voters decide whether to consume news or not

b Candidates propose policies3. State is realized4. Voters observe signal realizations and policies and vote

expressively; winner is determined by simple majority rule witheven tie-breaking

Equilibrium

Under segmentation technology S, a policy profile 〈−a, a〉 andnews profile µ can be attained in a PBE if

µ is a |S|-dimensional random variable, where the marginaldistribution of each dimension s ∈ S solves problem (s),taking 〈−a, a〉 as givena maximizes candidate R’s winning probability, taking µ,candidate L’s policy −a and voters’ behaviors in stages 2(a)and 4 of the game as given

Remark 1.Assume for now that news signals are conditionally independentacross market segments.

Agenda


Key conceptsMain characterizationComparative statics


Key Concepts

A deviation a′ by candidate R from 〈−a, a〉 to a′ attracts type kvoters if

v(−a, a′, k

)+ µs

L (a, k) > 0

and it repels type k voters if

v(−a, a′, k

)+ µs

R (a, k) < 0

If a′ does not attract or repel type k voters, then it does not affectthe latter’s voting decisions

Key Concepts (Cont’d)

Define the k-proof set by

ΞS (k) =

a ≥ 0 : v (−a, t (k) , k) + µSL (a, k) ≤ 0

and type k voters’ policy latitude by

ξS (k) = max ΞS (k)

Key Concepts (Cont’d)

Under segmentation technology S and population function q,Let ES,q denote the set of policy a’s such that the symmetricpolicy profile 〈−a, a〉 can arise in equilibriumDefine aS,q = max ES,q as the degree of policy polarization

Type k voters are disciplining if aS,q = ξS (k)

Agenda




Main Characterization

Theorem 2.Assume Assumptions 1 and 2. Then under all segmentationtechnology S ∈ b, p and population function q, ES,q =

[0, aS,q

]and aS,q > 0. In particular,

(i) ab,q = ξb (0) ∀q;

(ii) ap,q =

ξp (0) if q (0) > 1/2,

mink∈K

ξp (k) if q (0) ≤ 1/2.

Proof Sketch: Broadcast News

Since all voters receive the same voting recommendation,

a deviation by candidate R is profitable⇐⇒ it attracts a majority of voters⇐⇒ it attracts median voters

Thus median voters are always disciplining, i.e.,

Eb,q = Ξb (0) ∀q

Proof Sketch: Personalized News

In the case where q (0) ≤ 1/2, a deviation is profitable if it attractsany type k voters, holding other things constant

Conditional independence implies that the above deviation strictlyincreases candidate R’s winning probability in the event where typek voters are pivotal

Interestingly, a policy profile can be attained in equilibrium if theabove deviation is unprofitable. In fact...

Proof Sketch: Personalized News

Lemma 3.Assume Assumptions 1 and 2. Then the following are equivalent inthe case where S = p and q (0) ≤ 1/2:

(i) 〈−a, a〉, a ≥ 0 can be attained in equilibrium;(ii) no unilateral deviation of candidate R to any a′ ∈ [−a, a]

attracts any voter whose bliss point lies in [−a, a].

Thus Ep,q = A (0) ∪ A (1), whereA (0) = [0, t (1)) ∩ Ξp (0)A (1) = [t (1) , a] ∩

⋂k∈K

Ξp (k)

Proof Sketch: Final Steps

Strict obedience =⇒ aS,q > 0 ∀S, q

Characterizing policy latitudes establishes the interval property andpins down the disciplining voter

Takeaway

News personalization makes attracting any type of voters—albeitnon-majorities—a profitable deviation

Voters with the smallest policy latitude are the most susceptible topolicy deviations and therefore constitute the disciplining entity forequilibrium polarization

Deviations could be more effective in the personalized case than inthe broadcast case

Who are Disciplining Under Personalized News?

Lemma 4.When a is large,

(i) if ξb (0) ≥ t (1), then ξb (0) = −µbL := µb

L (t (1));(ii) ξp (k) = −

[t (k) + µp

L (k)], where µp

L (k) := µpL (|t (k) |, k).

Policy preference vs. belief about fitness:Right-wing (base) voters most prefer candidate R policy-wisebut are the most pessimistic when news is unfavorableThe opposite is true for left-wing (opposition) votersBase voters have a bigger policy latitude than oppositionvoters if and only if

µpL (1) + µp

R (1) < −2t (1) . (∗)

Agenda




The Politics of News Personalization

Proposition 1.Fix any population function q, assume Assumptions 1 and 2 andlet a be large. Then news personalization strictly increases policypolarization if and only if under personalized news, one of thefollowing conditions hold:

(i) median voters are disciplining;(ii) extreme voters are disciplining and have a bigger policy

latitude than the median voters hearing broadcast news, i.e.,

ξb (0) < min ξp (1) , ξp (−1) . (∗∗)

The Politics of News Personalization (Cont’d)

Proposition 1 (cont’d).Condition (∗∗) holds if ξb (0) < t (1). When ξb (0) ≥ t (1),(a) if right-wing voters are disciplining under personalized news,

i.e., (∗) is violated, then (∗∗) is equivalent to

µpL (1)− µb

L < −t (1) ;

(b) if left-wing voters are disciplining under personalized news,i.e., (∗) is satisfied, then (∗∗) is equivalent to

t (−1) < µbL − µ

pL (−1) .

Illustrative Example

Example 1.In the case where h (µ) = µ2, we have that

ξb (0) =(

1 +√

1− 16λt (1))/ (4λ) > t (1) ,

and that

ξp (k) =

1/ (2λ)− 3t (1) if k = −1,1/ (2λ) if k = 0,1/ (2λ)− t (1) if k = 1.

Thus,Left-wing voters have the smallest policy latitude, followed byright-wing voters and then median votersPersonalization increases policy polarization if and only ifλt (1) > 1/18

Mass Polarization vs. Elite Polarization

Define increasing mass polarization by adding mean-preservingspreads to voters’ type distribution as suggested by Fiorina andAbrams (2008) and Gentzkow (2016)

Proposition 2.Assume Assumptions 1 and 2. Then ap,q ≥ ap,q′ for all populationfunctions q and q′ such that q (0) > q′ (0), and the inequality isstrict if and only if q (0) > 1/2 ≥ q′ (0) and 0 /∈ arg min

k∈Kξp (k).

Marginal Attention Cost and Regulatory Implications

Allow perfect competition between infomediaries⇐⇒ increase λ in the personalized case=⇒ reduce policy polarization

Proposition 3.Assume Assumption 1 and take any λ′ > λ > 0 that satisfyAssumption 2. Then for all a ≥ 0 and k ∈ K, we have thatµp

L (a, k, λ) < µpL (a, k, λ′) and that µp

R (a, k, λ) > µpR (a, k, λ′).

Agenda

1. Model2. Extensions3. Literature

General Model

In general, the set of policy a’s that can be attained in equilibriumis [

0, minC′s formed under 〈χ,q〉

ξS (C)]

whereC: influential coalition example

ξS (C): policy latitude of influential coalition Cχ: news configuration example

Thus,Joint news distribution affects polarization through χ,whereas marginal distributions do so through ξS (·)Enriching influential coalitions reduces polarization, holdingother things constant

General Model (Cont’d)

In the personalized case,Relaxing conditional independence can only increasepolarizationmink∈K ξ

p (k) is the exact lower bound for the polarizationthat can be attained across all scenarios

Skewness is crucial for personalization to increase polarization

Other Extensions

General state distributionSkewness vs. level effectAlternative candidate motive· · ·

Literature

Media bias:Prat and Stromberg (2013), Stromberg (2015), Anderson et al.(2016)Mullainathan and Shleifer (2005), Bernhardt et al. (2008),Gentzkow and Shapiro (2010), Martin and Yurukoglu (2017)Calvert (1985a), Suen (2004), Burke (2008), Che and Mierendorff(2018)

Own-party bias and occasional big surprise:Fiorina and Abrams (2008), Barber and McCarty (2015), Gentzkow(2016)Chiang and Knight (2011), Flaxman et al. (2016)DellaVigna and Gentzkow (2010)

Literature (Cont’d)

Rational inattention:Sims (1998, 2003), Matejka and Mckay (2015), Caplin (2016),Mackowiak et al. (2018)Caplin and Dean (2015), Zhong (2017), Denti (2018), Tsakas(2019), Caplin et al. (2019); Hebert and Woodford (2017), Morrisand Strack (2017); Dean and Nelighz (2019)Matejka and Tabellini (2016)

Media as flexible and profit-maximizing information channel:Stromberg (2004), Chan and Suen (2008), Yuksel and Perego (2018)

Literature (Cont’d)

Economics of news aggregators: Athey and Mobius (2012), Atheyet al. (2017), Chiou and Tucker (2017), Jeon (2018)

Strict obedience vs. continuous signal distribution: Calvert(1985b), Duggan (2000), Patty (2005), Duggan (2017)

Influential Coalitions

S = b S = pq(0) > 1/2 majorities majoritiesq(0) < 1/2 majorities 2K − ∅

Table 1: influential coalitions under any symmetric policy profile〈−a, a〉, a ≥ 0: baseline model.

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News Configuration (Cont’d)

When S = b,

χ∗ :=

0 10 1...

...0 1

News Configuration (Cont’d)

When S = p and signals are conditionally independent,

χ∗∗ :=

0 1 0 · · · 0 1 · · · 0 · · · 10 0 1 · · · 0 1 · · · 0 · · · 1...

...... · · ·

...... · · ·

... · · · 10 0 0 · · · 0 0 · · · 1 · · · 10 0 0 · · · 1 0 · · · 1 · · · 1

︸︷︷︸

2|K| columns

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the politics of news personalization · the politics of news personalization lin hu 1 anqi li 2...

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