The Politics of News Personalization Lin Hu 1 Anqi Li 2 Ilya Segal 3 1 Australian National University 2 Washington University in St. Louis 3 Stanford University October 2019
Changing News Landscape Increasing online news consumption via social media and mobile devices: In 2016, 40% Americans frequently consulted online news sources, 62% got news on social media and 18% did so often In 2017, 85% U.S. adults got news on mobile devices In 2018, social media outpaced print newspapers as a news source
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 25,000 publishers in 2013 The top 3 popular news websites in 2019: Yahoo! News, Google News and Huffington Post, are aggregators Social 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 own bubbles, ...especially our social media feeds, surrounded by people who look like us and share the same political outlook and never challenge our assumptions... And increasingly, we become so secure in our bubbles that we start accepting only information, whether it’s true or not, that fits our opinions, instead of basing our 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 by rational inattentive voters in equilibrium? How does news personalization affect policy polarization in a model of electoral competition?
Agenda 1. Model News aggregation Electoral competition 2. Extensions 3. Literature
Agenda 1. Model News aggregation Setup Optimal news signal Electoral competition 2. Extensions 3. Literature
Political Players Two candidates L and R : Office-motivated Policy space: A = [ − a , a ] Policy profile: a = � a L , a R � , fixed to any �− a , a � , a ≥ 0 for now A unit mass of voters: Types: K = {− 1 , 0 , 1 } Population function: q : K → R + , q ( − k ) = q ( k ) Valuation of policies: u ( a , k ) = −| t ( k ) − a | , t : K → R is strictly increasing and t ( k ) = − t ( − k )
Expressive Voting Utility difference from choosing candidate R over candidate L : v ( a , k ) + ω where v ( a , k ) = u ( a R , k ) − u ( a L , k ) ω : valence state about fitness for office: E.g., whether the state favors experience with the use of hard or soft power Equal ± 1 with prob. . 5
News Aggregation A monopolistic infomediary partitions K into market segments using segmentation technology S : Broadcast news: b = {K} Personalized 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 realizations Π ( · | ω ): probability distribution over Z conditional on the state being ω
News Consumption Each voter can either consume the news signal offered to him or abstain Consume news = absorb the information contained in the news signal: Potential gain from improved expressive voting Attention cost: λ · I (Π) Infomediary’s gross profit = total amount of attention paid by voters
Model Discussion: News Signal Under signal structure Π : Ω → ∆ ( Z ), π z : prob. that the news realization is z µ z : posterior mean of the state given news realization z Strictly prefer candidate R to L iff v ( a , k ) + µ z > 0 —————– candidate L to R iff v ( a , k ) + µ z < 0 Bayes’ plausibility: � π z · µ z = 0 z ∈Z 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) = π z · h ( µ z ) , z ∈Z 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. � � 1+ µ E.g., h ( µ ) = µ 2 ; h ( µ ) = H , H = binary entropy function 2
Model Discussion: Miscellaneous Voter’s inflexibility Attention-based business model Ability to personalize
Agenda 1. Model News aggregation Setup Optimal news signal Electoral competition 2. Extensions 3. Literature
Optimal News Signal Expected utility gain from news consumption: � π z [ v ( a , k ) + µ z ] + if k ≤ 0 z ∈Z V (Π; a , k ) = � π z [ v ( a , k ) + µ z ] − − if k > 0 z ∈Z Under segmentation technology S , any optimal news signal of market segment s ∈ S solves � max Π I (Π) · q ( k , s ) ( s ) k ∈K : V (Π; a , k ) ≥ λ · I (Π)
Binary Recommendations and Strict Obedience For binary news signals, write Z = { L , R } and assume w.l.o.g. that µ L < 0 < µ R A binary news signal induces strict obedience if the following holds among its consumers: v ( a , k ) + µ L < 0 < v ( a , k ) + µ R (SOB)
Binary Recommendations and Strict Obedience (Cont’d) Lemma 1. Fix any symmetric policy profile �− a , a � , a ≥ 0 and assume Assumption 1. Then, (i) any optimal broadcast news signal is either degenerate or binary; (ii) any optimal personalized news signal of any type of voters is either 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 assume Assumption 1. Then, (i) in the broadcast case, if it is optimal to induce consumption from all voters, then the optimal news signal is unique; (ii) the optimal personalized news signal of any type of voters is unique.
Regularity Condition Assumption 2. Under any symmetric policy profile �− a , a � , a ≥ 0 , (i) any optimal news signal is nondegenerate, and the posterior means of the state conditional on its realizations belong to the open interval ( − 1 , 1) ; (ii) it is optimal to induce consumption from all voters in the broadcast case.
Notations Under segmentation technology S : Π S ( a , k ): optimal news signal consumed by type k voters µ S z ( a , k ): the posterior mean of the state given news realization z ∈ { L , R } µ S L ( a , k ) π S ( a , k ) = − L ( a , k ) : prob. that candidate R is µ S R ( a , k ) − µ S endorsed Suppress the notation of k if S = b
Own-Party Bias and Occasional Big Surprise 1 △ News signal ( B ) □ News signal for k < 0 ( P ) 0.8 ◇ News signal for k = 0 ( P ) ☒ News signal for k > 0 ( P ) 0.6 ◇ □ μ R 0.4 ☒ △ 0.2 0 - 1 - 0.8 - 0.6 - 0.4 - 0.2 0 μ L 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 assume Assumptions 1 and 2. Then, (i) π b ( a ) = 1 / 2 and µ b L ( a ) + µ b R ( a ) = 0 ; (ii) ∀ k ∈ K , µ p L ( a , − k ) + µ p R ( a , k ) = 0 , and (a) π p ( a , k ) < 1 / 2 and µ p L ( a , k ) + µ p R ( a , k ) > 0 if k < 0 ; (b) π p ( a , k ) = 1 / 2 and µ p L ( a , k ) + µ p R ( a , k ) = 0 if k = 0 ; (c) π p ( a , k ) > 1 / 2 and µ p L ( a , k ) + µ p R ( a , k ) < 0 if k > 0 ; � � (iii) I (Π p ( a , k )) > I Π b ( a ) ∀ k ∈ K .
Agenda 1. Model News personalization Electoral Competition 2. Extensions 3. Literature
Game Sequence 1. The infomediary commits to news signals 2. a Voters decide whether to consume news or not b Candidates propose policies 3. State is realized 4. Voters observe signal realizations and policies and vote expressively; winner is determined by simple majority rule with even tie-breaking
Equilibrium Under segmentation technology S , a policy profile �− a , a � and news profile � µ can be attained in a PBE if µ is a |S| -dimensional random variable, where the marginal � distribution of each dimension s ∈ S solves problem ( s ), taking �− a , a � as given a 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 independent across market segments.
Agenda 1. Model News personalization Electoral Competition Key concepts Main characterization Comparative statics 2. Extensions 3. Literature
Key Concepts A deviation a ′ by candidate R from �− a , a � to a ′ attracts type k voters if � + µ s � − a , a ′ , k v L ( a , k ) > 0 and it repels type k voters if � + µ s � − a , a ′ , k v R ( a , k ) < 0 If a ′ does not attract or repel type k voters, then it does not affect the latter’s voting decisions
Key Concepts (Cont’d) Define the k -proof set by � � Ξ S ( k ) = a ≥ 0 : v ( − a , t ( k ) , k ) + µ S L ( a , k ) ≤ 0 and type k voters’ policy latitude by ξ S ( k ) = max Ξ S ( k )
Key Concepts (Cont’d) Under segmentation technology S and population function q , Let E S , q denote the set of policy a ’s such that the symmetric policy profile �− a , a � can arise in equilibrium Define a S , q = max E S , q as the degree of policy polarization Type k voters are disciplining if a S , q = ξ S ( k )
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